{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example_Basic scorecard modeling with Scorecard-Bundle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Data-Preparation\" data-toc-modified-id=\"Data-Preparation-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Data Preparation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Download-sample-data\" data-toc-modified-id=\"Download-sample-data-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Download sample data</a></span></li><li><span><a href=\"#Handle-categorical-features-and-missing-values\" data-toc-modified-id=\"Handle-categorical-features-and-missing-values-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Handle categorical features and missing values</a></span></li><li><span><a href=\"#Define-feature-and-target\" data-toc-modified-id=\"Define-feature-and-target-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Define feature and target</a></span></li></ul></li><li><span><a href=\"#Scorecard-Model\" data-toc-modified-id=\"Scorecard-Model-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Scorecard Model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Feature-Discretization-with-ChiMerge\" data-toc-modified-id=\"Feature-Discretization-with-ChiMerge-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Feature Discretization with ChiMerge</a></span></li><li><span><a href=\"#Feature-Encoding-with-Weight-of-Evidence-(WOE)\" data-toc-modified-id=\"Feature-Encoding-with-Weight-of-Evidence-(WOE)-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Feature Encoding with Weight of Evidence (WOE)</a></span></li><li><span><a href=\"#Feature-Selection\" data-toc-modified-id=\"Feature-Selection-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Feature Selection</a></span></li><li><span><a href=\"#Model-Training\" data-toc-modified-id=\"Model-Training-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Model Training</a></span></li><li><span><a href=\"#Model-Evaluation\" data-toc-modified-id=\"Model-Evaluation-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Model Evaluation</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:45:34.765016Z",
     "start_time": "2019-08-13T07:45:28.696724Z"
    }
   },
   "outputs": [],
   "source": [
    "# The folowing codes for downloading housing price dataset is from \n",
    "# Aurélien Géron's book \"Hands on Machine Learning with Scikit-learn and Tensorflow\"\n",
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = \"datasets/housing\"\n",
    "HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + \"/housing.tgz\"\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()\n",
    "\n",
    "import pandas as pd\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)\n",
    "\n",
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:45:34.972569Z",
     "start_time": "2019-08-13T07:45:34.776044Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
      "       'total_bedrooms', 'population', 'households', 'median_income',\n",
      "       'median_house_value', 'ocean_proximity'],\n",
      "      dtype='object')\n",
      "shape: (20640, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "print(housing.columns)\n",
    "print('shape:',housing.shape)\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle categorical features and missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:45:34.996633Z",
     "start_time": "2019-08-13T07:45:34.980589Z"
    }
   },
   "outputs": [],
   "source": [
    "housing.drop(['ocean_proximity'], axis=1, inplace=True) # drop the categorical feature for simplicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:45:35.030726Z",
     "start_time": "2019-08-13T07:45:35.001644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.isna().sum() # check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:45:35.051798Z",
     "start_time": "2019-08-13T07:45:35.036744Z"
    }
   },
   "outputs": [],
   "source": [
    "housing.fillna(value=housing.total_bedrooms.median(), inplace=True) # fill the missing `total_bedrooms` with its median for simplicity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define feature and target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let `median_house_value` be the target and all other columns be features\n",
    "\n",
    "\n",
    "- No train test splitting here for simplicity\n",
    "\n",
    "\n",
    "- Set y=1 when medain house value is larger than its q90 and y=0 otherwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:45:35.665411Z",
     "start_time": "2019-08-13T07:45:35.059800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e64f9da20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-colorblind') # Set style for matplotlib\n",
    "plt.rcParams['savefig.dpi'] = 300 # dpi of diagrams\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "\n",
    "housing.median_house_value.hist(bins=30)\n",
    "plt.axvline(x=housing.median_house_value.quantile(0.9), c='r', alpha=0.5)\n",
    "plt.annotate(s='Q90',xy=(housing.median_house_value.quantile(0.9),1200),\n",
    "            xytext=(housing.median_house_value.quantile(0.9)+50000,1300),\n",
    "            arrowprops={'arrowstyle':'->'})\n",
    "plt.title('Distribution of median house value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Median house value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:45:35.706523Z",
     "start_time": "2019-08-13T07:45:35.671430Z"
    }
   },
   "outputs": [],
   "source": [
    "features = list(set(housing.columns) - set(['median_house_value'])) # feature name list\n",
    "q90 = housing.median_house_value.quantile(0.9)\n",
    "X, y = housing[features], housing.median_house_value.map(lambda x: 1 if x>q90 else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scorecard Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:45:39.614914Z",
     "start_time": "2019-08-13T07:45:35.711541Z"
    }
   },
   "outputs": [],
   "source": [
    "#import sys\n",
    "#sys.path.append('D:\\Github\\Scorecard-Bundle')\n",
    "from scorecardbundle.feature_discretization import ChiMerge as cm\n",
    "from scorecardbundle.feature_encoding import WOE as woe\n",
    "from scorecardbundle.feature_selection import FeatureSelection as fs\n",
    "from scorecardbundle.model_training import LogisticRegressionScoreCard as lrsc\n",
    "from scorecardbundle.model_evaluation import ModelEvaluation as me"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Discretization with ChiMerge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:48:50.243204Z",
     "start_time": "2019-08-13T07:45:39.620932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>median_income</th>\n",
       "      <th>households</th>\n",
       "      <th>population</th>\n",
       "      <th>total_rooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-inf~335.0</td>\n",
       "      <td>37.6~37.99</td>\n",
       "      <td>36.0~51.0</td>\n",
       "      <td>-122.62~-121.58000000000001</td>\n",
       "      <td>7.7197~inf</td>\n",
       "      <td>-inf~248.0</td>\n",
       "      <td>-inf~873.0</td>\n",
       "      <td>319.0~1176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>685.0~2216.270000000004</td>\n",
       "      <td>37.6~37.99</td>\n",
       "      <td>19.0~36.0</td>\n",
       "      <td>-122.62~-121.58000000000001</td>\n",
       "      <td>7.7197~inf</td>\n",
       "      <td>853.0~1257.4399999999987</td>\n",
       "      <td>1529.0~3570.0</td>\n",
       "      <td>2657.0~inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-inf~335.0</td>\n",
       "      <td>37.6~37.99</td>\n",
       "      <td>51.0~52.0</td>\n",
       "      <td>-122.62~-121.58000000000001</td>\n",
       "      <td>5.753465~7.7197</td>\n",
       "      <td>-inf~248.0</td>\n",
       "      <td>-inf~873.0</td>\n",
       "      <td>1176.0~2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-inf~335.0</td>\n",
       "      <td>37.6~37.99</td>\n",
       "      <td>51.0~52.0</td>\n",
       "      <td>-122.62~-121.58000000000001</td>\n",
       "      <td>3.9669399999999992~5.753465</td>\n",
       "      <td>-inf~248.0</td>\n",
       "      <td>-inf~873.0</td>\n",
       "      <td>1176.0~2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-inf~335.0</td>\n",
       "      <td>37.6~37.99</td>\n",
       "      <td>51.0~52.0</td>\n",
       "      <td>-122.62~-121.58000000000001</td>\n",
       "      <td>3.0083~3.9669399999999992</td>\n",
       "      <td>248.0~260.0</td>\n",
       "      <td>-inf~873.0</td>\n",
       "      <td>1176.0~2012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_bedrooms    latitude housing_median_age  \\\n",
       "0               -inf~335.0  37.6~37.99          36.0~51.0   \n",
       "1  685.0~2216.270000000004  37.6~37.99          19.0~36.0   \n",
       "2               -inf~335.0  37.6~37.99          51.0~52.0   \n",
       "3               -inf~335.0  37.6~37.99          51.0~52.0   \n",
       "4               -inf~335.0  37.6~37.99          51.0~52.0   \n",
       "\n",
       "                     longitude                median_income  \\\n",
       "0  -122.62~-121.58000000000001                   7.7197~inf   \n",
       "1  -122.62~-121.58000000000001                   7.7197~inf   \n",
       "2  -122.62~-121.58000000000001              5.753465~7.7197   \n",
       "3  -122.62~-121.58000000000001  3.9669399999999992~5.753465   \n",
       "4  -122.62~-121.58000000000001    3.0083~3.9669399999999992   \n",
       "\n",
       "                 households     population    total_rooms  \n",
       "0                -inf~248.0     -inf~873.0   319.0~1176.0  \n",
       "1  853.0~1257.4399999999987  1529.0~3570.0     2657.0~inf  \n",
       "2                -inf~248.0     -inf~873.0  1176.0~2012.0  \n",
       "3                -inf~248.0     -inf~873.0  1176.0~2012.0  \n",
       "4               248.0~260.0     -inf~873.0  1176.0~2012.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_cm = cm.ChiMerge(max_intervals=5, min_intervals=2, output_dataframe=True)\n",
    "result_cm = trans_cm.fit_transform(X, y) \n",
    "result_cm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:48:50.265261Z",
     "start_time": "2019-08-13T07:48:50.249220Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total_bedrooms': array([ 335.  ,  341.  ,  685.  , 2216.27,     inf]),\n",
       " 'latitude': array([34.49, 36.97, 37.6 , 37.99,   inf]),\n",
       " 'housing_median_age': array([ 9., 19., 36., 51., 52.]),\n",
       " 'longitude': array([-122.62, -121.58, -118.98, -118.37,     inf]),\n",
       " 'median_income': array([3.0083  , 3.96694 , 5.753465, 7.7197  ,      inf]),\n",
       " 'households': array([ 248.  ,  260.  ,  853.  , 1257.44,     inf]),\n",
       " 'population': array([ 873.,  992., 1529., 3570.,   inf]),\n",
       " 'total_rooms': array([ 319., 1176., 2012., 2657.,   inf])}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_cm.boundaries_ # show boundaries for all features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Encoding with Weight of Evidence (WOE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:48:50.661324Z",
     "start_time": "2019-08-13T07:48:50.270272Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>median_income</th>\n",
       "      <th>households</th>\n",
       "      <th>population</th>\n",
       "      <th>total_rooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.053637</td>\n",
       "      <td>0.304771</td>\n",
       "      <td>0.123922</td>\n",
       "      <td>0.54122</td>\n",
       "      <td>3.808660</td>\n",
       "      <td>0.018522</td>\n",
       "      <td>0.322442</td>\n",
       "      <td>-0.695164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.090923</td>\n",
       "      <td>0.304771</td>\n",
       "      <td>-0.033261</td>\n",
       "      <td>0.54122</td>\n",
       "      <td>3.808660</td>\n",
       "      <td>0.237510</td>\n",
       "      <td>-0.325632</td>\n",
       "      <td>0.314325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.053637</td>\n",
       "      <td>0.304771</td>\n",
       "      <td>1.127234</td>\n",
       "      <td>0.54122</td>\n",
       "      <td>1.295348</td>\n",
       "      <td>0.018522</td>\n",
       "      <td>0.322442</td>\n",
       "      <td>-0.234270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.053637</td>\n",
       "      <td>0.304771</td>\n",
       "      <td>1.127234</td>\n",
       "      <td>0.54122</td>\n",
       "      <td>-0.075948</td>\n",
       "      <td>0.018522</td>\n",
       "      <td>0.322442</td>\n",
       "      <td>-0.234270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.053637</td>\n",
       "      <td>0.304771</td>\n",
       "      <td>1.127234</td>\n",
       "      <td>0.54122</td>\n",
       "      <td>-0.870713</td>\n",
       "      <td>0.442561</td>\n",
       "      <td>0.322442</td>\n",
       "      <td>-0.234270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bedrooms  latitude  housing_median_age  longitude  median_income  \\\n",
       "0        0.053637  0.304771            0.123922    0.54122       3.808660   \n",
       "1        0.090923  0.304771           -0.033261    0.54122       3.808660   \n",
       "2        0.053637  0.304771            1.127234    0.54122       1.295348   \n",
       "3        0.053637  0.304771            1.127234    0.54122      -0.075948   \n",
       "4        0.053637  0.304771            1.127234    0.54122      -0.870713   \n",
       "\n",
       "   households  population  total_rooms  \n",
       "0    0.018522    0.322442    -0.695164  \n",
       "1    0.237510   -0.325632     0.314325  \n",
       "2    0.018522    0.322442    -0.234270  \n",
       "3    0.018522    0.322442    -0.234270  \n",
       "4    0.442561    0.322442    -0.234270  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_woe = woe.WOE_Encoder(output_dataframe=True)\n",
    "result_woe = trans_woe.fit_transform(result_cm, y) # WOE is fast. This only takes less then 1 seconds\n",
    "result_woe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:48:50.690391Z",
     "start_time": "2019-08-13T07:48:50.666327Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total_bedrooms': 0.013541575632127906,\n",
       " 'latitude': 0.7183847080349024,\n",
       " 'housing_median_age': 0.19060315707077985,\n",
       " 'longitude': 0.7654090245162248,\n",
       " 'median_income': 2.3397193770128966,\n",
       " 'households': 0.0135796280489273,\n",
       " 'population': 0.08695555095576324,\n",
       " 'total_rooms': 0.10662402115734344}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_woe.iv_ # the information value (iv) for each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:48:50.724482Z",
     "start_time": "2019-08-13T07:48:50.698424Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total_bedrooms': ({'-inf~335.0': 0.05363662574426767,\n",
       "   '2216.270000000004~inf': -0.8932794316948403,\n",
       "   '335.0~341.0': -0.6691884620736074,\n",
       "   '341.0~685.0': -0.05794484918568975,\n",
       "   '685.0~2216.270000000004': 0.09092250564300006},\n",
       "  0.013541575632127906),\n",
       " 'latitude': ({'-inf~34.49': 0.14396913031389935,\n",
       "   '34.49~36.97': -1.7569264297479703,\n",
       "   '36.97~37.6': 0.931717788490672,\n",
       "   '37.6~37.99': 0.3047708625525933,\n",
       "   '37.99~inf': -2.7389973720452145},\n",
       "  0.7183847080349024),\n",
       " 'housing_median_age': ({'-inf~9.0': -0.23835346407206823,\n",
       "   '19.0~36.0': -0.03326078999759868,\n",
       "   '36.0~51.0': 0.12392224123497612,\n",
       "   '51.0~52.0': 1.1272337015266705,\n",
       "   '9.0~19.0': -0.607474853400939},\n",
       "  0.19060315707077985),\n",
       " 'longitude': ({'-118.37~inf': -0.3775625638820992,\n",
       "   '-118.97999999999999~-118.37': 1.3515459501394256,\n",
       "   '-121.58000000000001~-118.97999999999999': -1.6298897437321815,\n",
       "   '-122.62~-121.58000000000001': 0.5412198652392345,\n",
       "   '-inf~-122.62': -2.6289494331299},\n",
       "  0.7654090245162248),\n",
       " 'median_income': ({'-inf~3.0083': -2.097070400737622,\n",
       "   '3.0083~3.9669399999999992': -0.8707126570874082,\n",
       "   '3.9669399999999992~5.753465': -0.07594765036737029,\n",
       "   '5.753465~7.7197': 1.2953483794588834,\n",
       "   '7.7197~inf': 3.808659723318916},\n",
       "  2.3397193770128966),\n",
       " 'households': ({'-inf~248.0': 0.01852150817161554,\n",
       "   '1257.4399999999987~inf': -0.3136703825650484,\n",
       "   '248.0~260.0': 0.4425611479893831,\n",
       "   '260.0~853.0': -0.032828802944417854,\n",
       "   '853.0~1257.4399999999987': 0.2375099970837297},\n",
       "  0.0135796280489273),\n",
       " 'population': ({'-inf~873.0': 0.3224417793753003,\n",
       "   '1529.0~3570.0': -0.3256318994038957,\n",
       "   '3570.0~inf': -0.8595473141174292,\n",
       "   '873.0~992.0': 0.13994394383986966,\n",
       "   '992.0~1529.0': -0.07486068372613865},\n",
       "  0.08695555095576324),\n",
       " 'total_rooms': ({'-inf~319.0': -0.037613321777362395,\n",
       "   '1176.0~2012.0': -0.2342701386348836,\n",
       "   '2012.0~2657.0': 0.07215494119163691,\n",
       "   '2657.0~inf': 0.31432487492220645,\n",
       "   '319.0~1176.0': -0.6951639484712919},\n",
       "  0.10662402115734344)}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_woe.result_dict_ # the WOE dictionary and iv for each feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Selection is an important step for Scorecard modelding and should at least filter out the features with too little predictabilty (e.g. iv<0.02) and the features that are causing co-linearity problem (e.g. Use VIF or Pearson correlation coefficient);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:48:50.842806Z",
     "start_time": "2019-08-13T07:48:50.730500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>factor</th>\n",
       "      <th>IV</th>\n",
       "      <th>woe_dict</th>\n",
       "      <th>corr_with</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>median_income</td>\n",
       "      <td>2.339719</td>\n",
       "      <td>{'-inf~3.0083': -2.097070400737622, '3.0083~3....</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>longitude</td>\n",
       "      <td>0.765409</td>\n",
       "      <td>{'-118.37~inf': -0.3775625638820992, '-118.979...</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>latitude</td>\n",
       "      <td>0.718385</td>\n",
       "      <td>{'-inf~34.49': 0.14396913031389935, '34.49~36....</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>0.190603</td>\n",
       "      <td>{'-inf~9.0': -0.23835346407206823, '19.0~36.0'...</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>0.106624</td>\n",
       "      <td>{'-inf~319.0': -0.037613321777362395, '1176.0~...</td>\n",
       "      <td>{'population': -0.6733932738977705}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>population</td>\n",
       "      <td>0.086956</td>\n",
       "      <td>{'-inf~873.0': 0.3224417793753003, '1529.0~357...</td>\n",
       "      <td>{'total_rooms': -0.6733932738977705}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>households</td>\n",
       "      <td>0.013580</td>\n",
       "      <td>{'-inf~248.0': 0.01852150817161554, '1257.4399...</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>0.013542</td>\n",
       "      <td>{'-inf~335.0': 0.05363662574426767, '2216.2700...</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               factor        IV  \\\n",
       "4       median_income  2.339719   \n",
       "3           longitude  0.765409   \n",
       "1            latitude  0.718385   \n",
       "2  housing_median_age  0.190603   \n",
       "7         total_rooms  0.106624   \n",
       "6          population  0.086956   \n",
       "5          households  0.013580   \n",
       "0      total_bedrooms  0.013542   \n",
       "\n",
       "                                            woe_dict  \\\n",
       "4  {'-inf~3.0083': -2.097070400737622, '3.0083~3....   \n",
       "3  {'-118.37~inf': -0.3775625638820992, '-118.979...   \n",
       "1  {'-inf~34.49': 0.14396913031389935, '34.49~36....   \n",
       "2  {'-inf~9.0': -0.23835346407206823, '19.0~36.0'...   \n",
       "7  {'-inf~319.0': -0.037613321777362395, '1176.0~...   \n",
       "6  {'-inf~873.0': 0.3224417793753003, '1529.0~357...   \n",
       "5  {'-inf~248.0': 0.01852150817161554, '1257.4399...   \n",
       "0  {'-inf~335.0': 0.05363662574426767, '2216.2700...   \n",
       "\n",
       "                              corr_with  \n",
       "4                                    {}  \n",
       "3                                    {}  \n",
       "1                                    {}  \n",
       "2                                    {}  \n",
       "7   {'population': -0.6733932738977705}  \n",
       "6  {'total_rooms': -0.6733932738977705}  \n",
       "5                                    {}  \n",
       "0                                    {}  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.selection_with_iv_corr(trans_woe, result_woe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Simplicity, we do not exclude any feature here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:48:51.183713Z",
     "start_time": "2019-08-13T07:48:50.847814Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Coding\\Python\\install\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "model = lrsc.LogisticRegressionScoreCard(trans_woe, PDO=-20, basePoints=100, verbose=True)\n",
    "model.fit(result_woe, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access the Scorecard rule table by attribute `woe_df_`. This is the Scorecard model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:48:51.224824Z",
     "start_time": "2019-08-13T07:48:51.187720Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>value</th>\n",
       "      <th>woe</th>\n",
       "      <th>beta</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>-inf~335.0</td>\n",
       "      <td>0.053637</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>2216.270000000004~inf</td>\n",
       "      <td>-0.893279</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>335.0~341.0</td>\n",
       "      <td>-0.669188</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>-16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>341.0~685.0</td>\n",
       "      <td>-0.057945</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>685.0~2216.270000000004</td>\n",
       "      <td>0.090923</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>latitude</td>\n",
       "      <td>-inf~34.49</td>\n",
       "      <td>0.143969</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>latitude</td>\n",
       "      <td>34.49~36.97</td>\n",
       "      <td>-1.756926</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>latitude</td>\n",
       "      <td>36.97~37.6</td>\n",
       "      <td>0.931718</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>latitude</td>\n",
       "      <td>37.6~37.99</td>\n",
       "      <td>0.304771</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>latitude</td>\n",
       "      <td>37.99~inf</td>\n",
       "      <td>-2.738997</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>-24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>-inf~9.0</td>\n",
       "      <td>-0.238353</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>19.0~36.0</td>\n",
       "      <td>-0.033261</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>36.0~51.0</td>\n",
       "      <td>0.123922</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>51.0~52.0</td>\n",
       "      <td>1.127234</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>9.0~19.0</td>\n",
       "      <td>-0.607475</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-118.37~inf</td>\n",
       "      <td>-0.377563</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-118.97999999999999~-118.37</td>\n",
       "      <td>1.351546</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-121.58000000000001~-118.97999999999999</td>\n",
       "      <td>-1.629890</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>-17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-122.62~-121.58000000000001</td>\n",
       "      <td>0.541220</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-inf~-122.62</td>\n",
       "      <td>-2.628949</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>-36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>median_income</td>\n",
       "      <td>-inf~3.0083</td>\n",
       "      <td>-2.097070</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>-44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>median_income</td>\n",
       "      <td>3.0083~3.9669399999999992</td>\n",
       "      <td>-0.870713</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>median_income</td>\n",
       "      <td>3.9669399999999992~5.753465</td>\n",
       "      <td>-0.075948</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>median_income</td>\n",
       "      <td>5.753465~7.7197</td>\n",
       "      <td>1.295348</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>median_income</td>\n",
       "      <td>7.7197~inf</td>\n",
       "      <td>3.808660</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>households</td>\n",
       "      <td>-inf~248.0</td>\n",
       "      <td>0.018522</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>households</td>\n",
       "      <td>1257.4399999999987~inf</td>\n",
       "      <td>-0.313670</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>households</td>\n",
       "      <td>248.0~260.0</td>\n",
       "      <td>0.442561</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>households</td>\n",
       "      <td>260.0~853.0</td>\n",
       "      <td>-0.032829</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>households</td>\n",
       "      <td>853.0~1257.4399999999987</td>\n",
       "      <td>0.237510</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>population</td>\n",
       "      <td>-inf~873.0</td>\n",
       "      <td>0.322442</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>population</td>\n",
       "      <td>1529.0~3570.0</td>\n",
       "      <td>-0.325632</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>population</td>\n",
       "      <td>3570.0~inf</td>\n",
       "      <td>-0.859547</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>-48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>population</td>\n",
       "      <td>873.0~992.0</td>\n",
       "      <td>0.139944</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>population</td>\n",
       "      <td>992.0~1529.0</td>\n",
       "      <td>-0.074861</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>-inf~319.0</td>\n",
       "      <td>-0.037613</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>1176.0~2012.0</td>\n",
       "      <td>-0.234270</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>2012.0~2657.0</td>\n",
       "      <td>0.072155</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>2657.0~inf</td>\n",
       "      <td>0.314325</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>319.0~1176.0</td>\n",
       "      <td>-0.695164</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>-34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature                                    value       woe  \\\n",
       "0       total_bedrooms                               -inf~335.0  0.053637   \n",
       "1       total_bedrooms                    2216.270000000004~inf -0.893279   \n",
       "2       total_bedrooms                              335.0~341.0 -0.669188   \n",
       "3       total_bedrooms                              341.0~685.0 -0.057945   \n",
       "4       total_bedrooms                  685.0~2216.270000000004  0.090923   \n",
       "5             latitude                               -inf~34.49  0.143969   \n",
       "6             latitude                              34.49~36.97 -1.756926   \n",
       "7             latitude                               36.97~37.6  0.931718   \n",
       "8             latitude                               37.6~37.99  0.304771   \n",
       "9             latitude                                37.99~inf -2.738997   \n",
       "10  housing_median_age                                 -inf~9.0 -0.238353   \n",
       "11  housing_median_age                                19.0~36.0 -0.033261   \n",
       "12  housing_median_age                                36.0~51.0  0.123922   \n",
       "13  housing_median_age                                51.0~52.0  1.127234   \n",
       "14  housing_median_age                                 9.0~19.0 -0.607475   \n",
       "15           longitude                              -118.37~inf -0.377563   \n",
       "16           longitude              -118.97999999999999~-118.37  1.351546   \n",
       "17           longitude  -121.58000000000001~-118.97999999999999 -1.629890   \n",
       "18           longitude              -122.62~-121.58000000000001  0.541220   \n",
       "19           longitude                             -inf~-122.62 -2.628949   \n",
       "20       median_income                              -inf~3.0083 -2.097070   \n",
       "21       median_income                3.0083~3.9669399999999992 -0.870713   \n",
       "22       median_income              3.9669399999999992~5.753465 -0.075948   \n",
       "23       median_income                          5.753465~7.7197  1.295348   \n",
       "24       median_income                               7.7197~inf  3.808660   \n",
       "25          households                               -inf~248.0  0.018522   \n",
       "26          households                   1257.4399999999987~inf -0.313670   \n",
       "27          households                              248.0~260.0  0.442561   \n",
       "28          households                              260.0~853.0 -0.032829   \n",
       "29          households                 853.0~1257.4399999999987  0.237510   \n",
       "30          population                               -inf~873.0  0.322442   \n",
       "31          population                            1529.0~3570.0 -0.325632   \n",
       "32          population                               3570.0~inf -0.859547   \n",
       "33          population                              873.0~992.0  0.139944   \n",
       "34          population                             992.0~1529.0 -0.074861   \n",
       "35         total_rooms                               -inf~319.0 -0.037613   \n",
       "36         total_rooms                            1176.0~2012.0 -0.234270   \n",
       "37         total_rooms                            2012.0~2657.0  0.072155   \n",
       "38         total_rooms                               2657.0~inf  0.314325   \n",
       "39         total_rooms                             319.0~1176.0 -0.695164   \n",
       "\n",
       "        beta  score  \n",
       "0   1.444781   14.0  \n",
       "1   1.444781  -25.0  \n",
       "2   1.444781  -16.0  \n",
       "3   1.444781   10.0  \n",
       "4   1.444781   16.0  \n",
       "5   0.463244   14.0  \n",
       "6   0.463244  -11.0  \n",
       "7   0.463244   25.0  \n",
       "8   0.463244   16.0  \n",
       "9   0.463244  -24.0  \n",
       "10  1.266568    4.0  \n",
       "11  1.266568   11.0  \n",
       "12  1.266568   17.0  \n",
       "13  1.266568   53.0  \n",
       "14  1.266568  -10.0  \n",
       "15  0.631033    5.0  \n",
       "16  0.631033   37.0  \n",
       "17  0.631033  -17.0  \n",
       "18  0.631033   22.0  \n",
       "19  0.631033  -36.0  \n",
       "20  0.931634  -44.0  \n",
       "21  0.931634  -11.0  \n",
       "22  0.931634   10.0  \n",
       "23  0.931634   47.0  \n",
       "24  0.931634  115.0  \n",
       "25  0.535186   13.0  \n",
       "26  0.535186    7.0  \n",
       "27  0.535186   19.0  \n",
       "28  0.535186   12.0  \n",
       "29  0.535186   16.0  \n",
       "30  2.433231   35.0  \n",
       "31  2.433231  -11.0  \n",
       "32  2.433231  -48.0  \n",
       "33  2.433231   22.0  \n",
       "34  2.433231    7.0  \n",
       "35  2.330646   10.0  \n",
       "36  2.330646   -4.0  \n",
       "37  2.330646   17.0  \n",
       "38  2.330646   33.0  \n",
       "39  2.330646  -34.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.woe_df_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scorecard should be applied on the **original feature values** (before discretization and WOE encoding)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T05:32:15.425210Z",
     "start_time": "2019-08-13T05:32:12.669768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>population</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>median_income</th>\n",
       "      <th>households</th>\n",
       "      <th>TotalScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   housing_median_age  longitude  population  total_rooms  latitude  \\\n",
       "0                17.0       22.0        35.0        -34.0      16.0   \n",
       "1                11.0       22.0       -11.0         33.0      16.0   \n",
       "2                53.0       22.0        35.0         -4.0      16.0   \n",
       "3                53.0       22.0        35.0         -4.0      16.0   \n",
       "4                53.0       22.0        35.0         -4.0      16.0   \n",
       "\n",
       "   total_bedrooms  median_income  households  TotalScore  \n",
       "0            14.0          115.0        13.0       198.0  \n",
       "1            16.0          115.0        16.0       218.0  \n",
       "2            14.0           47.0        13.0       196.0  \n",
       "3            14.0           10.0        13.0       159.0  \n",
       "4            14.0          -11.0        19.0       144.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = model.predict(X) # Scorecard should be applied on the original feature values\n",
    "result.head() # if model object's verbose parameter is set to False, predict will only return Total scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Users can manually adjust the Scorecard rules (as shown below, or output excel files to local position, edit it in excel and load it), and use `load_scorecard` parameter of predict() to load the adjusted rule table. See details in the documentation of `load_scorecard`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:52:35.676224Z",
     "start_time": "2019-08-13T07:52:35.553901Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Coding\\Python\\install\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>value</th>\n",
       "      <th>woe</th>\n",
       "      <th>beta</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>-inf~335.0</td>\n",
       "      <td>0.053637</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>2216.270000000004~inf</td>\n",
       "      <td>-0.893279</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>335.0~341.0</td>\n",
       "      <td>-0.669188</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>-16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>341.0~685.0</td>\n",
       "      <td>-0.057945</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>685.0~2216.270000000004</td>\n",
       "      <td>0.090923</td>\n",
       "      <td>1.444781</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>latitude</td>\n",
       "      <td>-inf~34.49</td>\n",
       "      <td>0.143969</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>latitude</td>\n",
       "      <td>34.49~36.97</td>\n",
       "      <td>-1.756926</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>latitude</td>\n",
       "      <td>36.97~37.6</td>\n",
       "      <td>0.931718</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>latitude</td>\n",
       "      <td>37.6~37.99</td>\n",
       "      <td>0.304771</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>latitude</td>\n",
       "      <td>37.99~inf</td>\n",
       "      <td>-2.738997</td>\n",
       "      <td>0.463244</td>\n",
       "      <td>-24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>-inf~9.0</td>\n",
       "      <td>-0.238353</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>19.0~36.0</td>\n",
       "      <td>-0.033261</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>36.0~51.0</td>\n",
       "      <td>0.123922</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>51.0~52.0</td>\n",
       "      <td>1.127234</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>9.0~19.0</td>\n",
       "      <td>-0.607475</td>\n",
       "      <td>1.266568</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-118.37~inf</td>\n",
       "      <td>-0.377563</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-118.97999999999999~-118.37</td>\n",
       "      <td>1.351546</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-121.58000000000001~-118.97999999999999</td>\n",
       "      <td>-1.629890</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>-17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-122.62~-121.58000000000001</td>\n",
       "      <td>0.541220</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>longitude</td>\n",
       "      <td>-inf~-122.62</td>\n",
       "      <td>-2.628949</td>\n",
       "      <td>0.631033</td>\n",
       "      <td>-36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>median_income</td>\n",
       "      <td>-inf~3.0083</td>\n",
       "      <td>-2.097070</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>-44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>median_income</td>\n",
       "      <td>3.0083~3.9669399999999992</td>\n",
       "      <td>-0.870713</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>median_income</td>\n",
       "      <td>3.9669399999999992~5.753465</td>\n",
       "      <td>-0.075948</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>median_income</td>\n",
       "      <td>5.753465~7.7197</td>\n",
       "      <td>1.295348</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>median_income</td>\n",
       "      <td>7.7197~inf</td>\n",
       "      <td>3.808660</td>\n",
       "      <td>0.931634</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>households</td>\n",
       "      <td>-inf~248.0</td>\n",
       "      <td>0.018522</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>households</td>\n",
       "      <td>1257.4399999999987~inf</td>\n",
       "      <td>-0.313670</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>households</td>\n",
       "      <td>248.0~260.0</td>\n",
       "      <td>0.442561</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>households</td>\n",
       "      <td>260.0~853.0</td>\n",
       "      <td>-0.032829</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>households</td>\n",
       "      <td>853.0~1257.4399999999987</td>\n",
       "      <td>0.237510</td>\n",
       "      <td>0.535186</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>population</td>\n",
       "      <td>-inf~873.0</td>\n",
       "      <td>0.322442</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>population</td>\n",
       "      <td>1529.0~3570.0</td>\n",
       "      <td>-0.325632</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>population</td>\n",
       "      <td>3570.0~inf</td>\n",
       "      <td>-0.859547</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>-48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>population</td>\n",
       "      <td>873.0~992.0</td>\n",
       "      <td>0.139944</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>population</td>\n",
       "      <td>992.0~1529.0</td>\n",
       "      <td>-0.074861</td>\n",
       "      <td>2.433231</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>-inf~319.0</td>\n",
       "      <td>-0.037613</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>1176.0~2012.0</td>\n",
       "      <td>-0.234270</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>2012.0~2657.0</td>\n",
       "      <td>0.072155</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>2657.0~inf</td>\n",
       "      <td>0.314325</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>319.0~1176.0</td>\n",
       "      <td>-0.695164</td>\n",
       "      <td>2.330646</td>\n",
       "      <td>-34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature                                    value       woe  \\\n",
       "0       total_bedrooms                               -inf~335.0  0.053637   \n",
       "1       total_bedrooms                    2216.270000000004~inf -0.893279   \n",
       "2       total_bedrooms                              335.0~341.0 -0.669188   \n",
       "3       total_bedrooms                              341.0~685.0 -0.057945   \n",
       "4       total_bedrooms                  685.0~2216.270000000004  0.090923   \n",
       "5             latitude                               -inf~34.49  0.143969   \n",
       "6             latitude                              34.49~36.97 -1.756926   \n",
       "7             latitude                               36.97~37.6  0.931718   \n",
       "8             latitude                               37.6~37.99  0.304771   \n",
       "9             latitude                                37.99~inf -2.738997   \n",
       "10  housing_median_age                                 -inf~9.0 -0.238353   \n",
       "11  housing_median_age                                19.0~36.0 -0.033261   \n",
       "12  housing_median_age                                36.0~51.0  0.123922   \n",
       "13  housing_median_age                                51.0~52.0  1.127234   \n",
       "14  housing_median_age                                 9.0~19.0 -0.607475   \n",
       "15           longitude                              -118.37~inf -0.377563   \n",
       "16           longitude              -118.97999999999999~-118.37  1.351546   \n",
       "17           longitude  -121.58000000000001~-118.97999999999999 -1.629890   \n",
       "18           longitude              -122.62~-121.58000000000001  0.541220   \n",
       "19           longitude                             -inf~-122.62 -2.628949   \n",
       "20       median_income                              -inf~3.0083 -2.097070   \n",
       "21       median_income                3.0083~3.9669399999999992 -0.870713   \n",
       "22       median_income              3.9669399999999992~5.753465 -0.075948   \n",
       "23       median_income                          5.753465~7.7197  1.295348   \n",
       "24       median_income                               7.7197~inf  3.808660   \n",
       "25          households                               -inf~248.0  0.018522   \n",
       "26          households                   1257.4399999999987~inf -0.313670   \n",
       "27          households                              248.0~260.0  0.442561   \n",
       "28          households                              260.0~853.0 -0.032829   \n",
       "29          households                 853.0~1257.4399999999987  0.237510   \n",
       "30          population                               -inf~873.0  0.322442   \n",
       "31          population                            1529.0~3570.0 -0.325632   \n",
       "32          population                               3570.0~inf -0.859547   \n",
       "33          population                              873.0~992.0  0.139944   \n",
       "34          population                             992.0~1529.0 -0.074861   \n",
       "35         total_rooms                               -inf~319.0 -0.037613   \n",
       "36         total_rooms                            1176.0~2012.0 -0.234270   \n",
       "37         total_rooms                            2012.0~2657.0  0.072155   \n",
       "38         total_rooms                               2657.0~inf  0.314325   \n",
       "39         total_rooms                             319.0~1176.0 -0.695164   \n",
       "\n",
       "        beta  score  \n",
       "0   1.444781  100.0  \n",
       "1   1.444781  -25.0  \n",
       "2   1.444781  -16.0  \n",
       "3   1.444781   10.0  \n",
       "4   1.444781   16.0  \n",
       "5   0.463244   14.0  \n",
       "6   0.463244  -11.0  \n",
       "7   0.463244   25.0  \n",
       "8   0.463244   16.0  \n",
       "9   0.463244  -24.0  \n",
       "10  1.266568    4.0  \n",
       "11  1.266568   11.0  \n",
       "12  1.266568   17.0  \n",
       "13  1.266568   53.0  \n",
       "14  1.266568  -10.0  \n",
       "15  0.631033    5.0  \n",
       "16  0.631033   37.0  \n",
       "17  0.631033  -17.0  \n",
       "18  0.631033   22.0  \n",
       "19  0.631033  -36.0  \n",
       "20  0.931634  -44.0  \n",
       "21  0.931634  -11.0  \n",
       "22  0.931634   10.0  \n",
       "23  0.931634   47.0  \n",
       "24  0.931634  115.0  \n",
       "25  0.535186   13.0  \n",
       "26  0.535186    7.0  \n",
       "27  0.535186   19.0  \n",
       "28  0.535186   12.0  \n",
       "29  0.535186   16.0  \n",
       "30  2.433231   35.0  \n",
       "31  2.433231  -11.0  \n",
       "32  2.433231  -48.0  \n",
       "33  2.433231   22.0  \n",
       "34  2.433231    7.0  \n",
       "35  2.330646   10.0  \n",
       "36  2.330646   -4.0  \n",
       "37  2.330646   17.0  \n",
       "38  2.330646   33.0  \n",
       "39  2.330646  -34.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc_table = model.woe_df_.copy()\n",
    "sc_table['score'][(sc_table.feature=='total_bedrooms') & (sc_table.value=='-inf~335.0')] = 100\n",
    "sc_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T07:54:05.283654Z",
     "start_time": "2019-08-13T07:54:02.481193Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>median_income</th>\n",
       "      <th>households</th>\n",
       "      <th>population</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>TotalScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bedrooms  latitude  housing_median_age  longitude  median_income  \\\n",
       "0           100.0      16.0                17.0       22.0          115.0   \n",
       "1            16.0      16.0                11.0       22.0          115.0   \n",
       "2           100.0      16.0                53.0       22.0           47.0   \n",
       "3           100.0      16.0                53.0       22.0           10.0   \n",
       "4           100.0      16.0                53.0       22.0          -11.0   \n",
       "\n",
       "   households  population  total_rooms  TotalScore  \n",
       "0        13.0        35.0        -34.0       284.0  \n",
       "1        16.0       -11.0         33.0       218.0  \n",
       "2        13.0        35.0         -4.0       282.0  \n",
       "3        13.0        35.0         -4.0       245.0  \n",
       "4        19.0        35.0         -4.0       230.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2 = model.predict(X, load_scorecard=sc_table) \n",
    "result2.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T05:32:15.442249Z",
     "start_time": "2019-08-13T05:32:15.432222Z"
    }
   },
   "outputs": [],
   "source": [
    "evaluation = me.BinaryTargets(y, result['TotalScore'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T05:32:15.489386Z",
     "start_time": "2019-08-13T05:32:15.449269Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6599261915746466"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation.ks_stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T05:32:18.598651Z",
     "start_time": "2019-08-13T05:32:15.500404Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS = 0.652\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15dcc708470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.9126749595837904\n"
     ]
    },
    {
     "data": {
      "image/png": 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T0tlxLIntxxLZfjSJrUcS2XzoJMnpgTGS1uV0UDc6jHrRYdStFEbd6DDqVAwlMiSIsCyJWliwI1timFmuljgp69asWUNsbCyrVq0CPANcRowYwVNPPUVkZKTf4grIhLGscbvdfPPNN5w4cYJNmzbRvHnzQtWfNGkS48aNY/LkybRu3ZqxY8cyYMAAlixZUjIB52LFihXceOON9OnTh+eee46XXnqJ2267jc8//7xA9Xfv3k3Xrl3p0aMHn3/+OX/++SejR49mz549/Pe///WdFx8fT//+/Tlw4MBp17DW0rt3b7Zu3cott9zC2rVrueyyy/j000/p1atXsb1XkXMtISWdP44ksOVQApsPJbDnRDJxKWnEJadzIjmduGTP6/iUdDI7dpPTMkpkkIfTYYgMCcLlNLicp7paPa8N0WHB1IgMxVXAljdXkOGCWlG0rVWR+pXDqRbhwqGkTyRPy5Yt8yWLzZo1Y9asWQHRMKKE8RxYvnw5J06cICQkhIULFxYqYYyLi2PixInce++9PPDAAwBER0fTqVMnli5dSpcuXUoq7GzGjx9Ps2bNmDNnDg6Hg86dO1OnTh1WrlxJx44dz1j/pZdeIioqijlz5uByuejWrRtut5shQ4YwefJkateuzeHDh+nZs+dpLY6Z/ve//7Fy5Uo2bdpElSpVALjyyiuZPn26EkbxC2stccnp7ItL5rg3sfMkeOmc8CZ5mVO7ZE7VkpDqzvb6WGIae+OSz1nM0WHBtKhegVpRp7pvAapFhHBl06r0aFKNyFB9NYj4y5AhQ/jwww/p1KkT48aN88vzirnRp8I5sHDhQho0aED79u1ZsGAB9913X6HqJiQk0L9/f19Zu3btcDgcrF+//owJ4/bt22nQoEGex7/55hu6d++e7zXcbjdLlixh7NixOByeVoWoqCguv/xyFi1aVKCE8eeff6Z79+7ZksEWLVoAsGPHDmrXrs2GDRu47LLLuO+++2jS5PQVHRs2bMg777zjSxYBatWqxcGDB087V6S4WWvZG5fMun3xLN9xjOU7j7N85zGOJqadufI5Vjk8mPqVw6kfHUb9yuE0rRZBi5hImsdUoFoFl0a+igSIkydP8uijj3L33XfTsmVLABwOB19++SVOp9PP0WWnhPEcWLhwId27d6d9+/b84x//IC0tjeDggs24um7dOsDTLJ3J6XTy1Vdf0bBhwzPWr1WrFqtXr87zeEFWsjl27BhJSUm0adMmW3ndunXZsmXLGeuDJ+bDhw9nK1u/fj0AderUAaBr165cdtll2Z7dzKpdu3bZ9n///XfmzZvHlClTChSDSEFkZFj2nEhm86GTbDgQz4b93p8DJzmeVPLJYc2oEBpUDic6LJioUM+8dlGhQVQMDaZCiBOnN9kLchqqRYQQU8FFTGQIlcOCcTo83cgRIfpoFwl0CxcuZOjQoWzfvp1ly5bx/fff+5LEQEsWQQljiYuLi2P58uUMGTKE9u3bc/LkSZYuXUq3bt0KVD+z9axy5crZynv27Fmg+i6Xi7Zt2xYu6BySkjwjJ3MOcAkLC8szucupW7dujB8/no8++oi+ffuyZcsWJkyYwIUXXkjdunUBfK2XZ3L06FEGDRrE4sWLiY2NJTY2tuBvRsql5DQ3RxJTOZKQ5t2m+p4J9KzkkcafRxL443ACfx5JJKWIA0QiXE6iQoOICgmiQkhQtnnyMkfgZs6vVyEkiAaVw2lSNYLGVSPUHSxSxh07dozRo0fzr3/9y1fmcDg4duwYVatW9WNk+dMnUwn75ptvSE9Pp3v37tStW5dq1aqxYMGCAieMKSkpQMGTqdykp+f9YLzTeeaZ8ENCQnznZuVyuXzJ5JmMGjWKBQsW0K9fP6Kjozl+/DjWWiZMmFCg+lmFhobSo0cP9uzZw0cffcTw4cO1hGM55s6wbD+ayE/bj7J0xzG2HEpg29FEdh5PKsKybPmrGuGic71oOterRJuaUdSuGErlcBcVQ4OIDAkiSFOxiEgu5s6dy7333sv+/fsBqFChApMmTWL48OFF+p4/F5QwlrCFCxcCZJseZsGCBUycOLFA9StUqABAQkJCtuH0V111FYMGDWLQoEH51i+OZxirVKmC0+lk797sqywePXqUiIiIM7wDj8jISL777ju+/fZbtm/f7nuQ90zx5yY8PJwHHniA+++/n0suuYRRo0b5/jtL2RefnM68Dft5d/Uefjt4kp3HkkpsUugIl5NWNSJpVT3Ss60RSesamfP96TlAESmY/fv3c9999/Hhhx/6yjKnp6tXr54fIys4JYwlbMGCBdx6662MGTMGgPnz5zN+/HiOHj1K5cqVCQkJOa0FMD093deqlzn4Y+vWrVxwwQWAZy7CRYsW0adPnzPevzieYXQ6nXTo0IEff/yRfv36+cpXrVp12nOF+THG0L17d5YvX87u3buZM2cOQUEF/yd47Ngx3G63r8ne6XRy7bXXMmPGjAJfQwJHSrrb+6xggu85wT0nkk+tMOJdWSQ1PYNUtyUpzc2xpLQidxdnCnc5iQwJooLLScWwYOpHh9G4agSNqkR4t+GcVylMU8CISJGNGzfOlyxGR0fz4osvMmjQoFL1h6cSxhK0Y8cOtmzZwmOPPeZ7jjAsLIxHH32Ur7/+mptvvpkGDRqwadOmbPV+++03X6tg5ozun3zyiS9h/Omnn7DW0r59+zPGUBzPMAL079+fyZMn8+CDD1KrVi2WLVvGihUr+Pvf/17oaz355JN06NCBAQMGFKreqFGjOHToULa5Hzdt2nTa5N4SGNLdGWw4EM/G/SfZdTyJXceT2H0i2ff64MnUYr9nqxqRtKtdkYZVwqkXHUZ4sOcxCleQgyrhLqp4l3erHO7CFRTY3T8iUnY8+eSTzJ07l8svv5xXXnnlnC+8URyUMJagzG7Srl27+sqaNWtG9erVWbBgATfffDN33HEHAwcO5LHHHuOKK65g8eLFLF68mDlz5gCeFsa7776bCRMmEBkZSePGjRk1ahRdunShU6dO5+y9DB06lOnTp3PRRRfRs2dPPvroI9q3b89NN90EeFo9165dS+PGjalUKe/VG5ctW8b8+fNZsmRJof+yuu+++7j44osZOnQoAwcOZOnSpXzwwQe8++67RXpvUnjWWk6muNl5PIntRxM9P8c8r/ecSCbFncFvB+JJSiu51UVaVq9A3/Nr0rhqBPUrh3F+zSgqh+c+h6eIyLmSkZHBW2+9xa233uqbQzEmJoY1a9ZQu3ZtP0d39pQwlqCFCxdy3nnn+UYBZ+ratasvmfzLX/7CkSNHePXVV3n22WepV68eL7/8Mrfeeqvv/Ndee42YmBgmTZpEXFwc119/PdOmTTun7yUqKoply5YxduxYli9fzm233caTTz7p61Leu3cvHTt2ZO7cufTu3TvP6/zjH/+gd+/eXHbZZYWOoUOHDsyfP59//vOfvP322zRu3Jg5c+Zk6yaXottzIokthxI4EJ/CgZMpHIhP4eDJ1Bz7KcWaDFYKC6ZV9Qo0rhpBWLATV5CDkGyrjBhCghxUCvO0DjaoHM75NSNLVXeOiJR9v//+O3fffTc//PADmzdv5qmnnvIdK83JIoCxtmQeFi/NjDGtgPXr16/X6Fsp86y1rNsXz6Ith/h4/X6+33q0WK9vDNSIDOG8SmHen1DqVDz1un7lcGpEhij5E5FSKy0tjeeee47x48f7Zjdp3Lgx69atOycrtWzYsIHWrVsDtLbWbiiJe6iFUaScOpGUxvgFvzNn9V4OxKcU6VqR3rkE61f2JIIup4O60WFcVLcSbWtXJCw48CahFREpDqtXryY2NtY3wNQYw8iRI5k4cWLALOtXHJQwipRDn248wLAP17LnRN5rGBsDVcJdxFRwUT0yhOoVQjzbyBBivK9reVclqRQWrBZCESlXkpOTeeKJJ3jmmWdwu92AZ8nb2bNnn3HZ3tJICaNIOXL4ZAr3f7yBd1bvOe2Yy+ng0gaVual1dXq3rkGtqFBNQC0ikoe+ffvyxRdfABAUFMTDDz/M2LFjfdPilTVKGEXKIGstfxxOYM3eOHYeT2LnMc9UNt9tPcrhhOzT2VzZtCoPdm/EpQ0qE+7SR4KISEGMHj2aL774gvbt2/PGG2/Qpk0bf4dUovTtIFJGnExJZ/GWw3z5+0G+3HSIbUcT8z2/Ulgwz9/Ykjs7nqfuZBGRM1i3bh3nn3++b79Hjx588cUX9OzZs1CLUJRWZf8dipRhfx5OYO66/Xy+6QA/bDta4LWTb2pVnWn92lCrYtl5IFtEpCQcOXKEBx54gLfeeosvv/zSt6AGwDXXXOPHyM4tJYwipUhKupufd51g8R+H+d/afazZG3fGOtUquKhbKYy60Z4RzNc0q8Y1zWPUqigikg9rLf/73/8YMWIEBw8eBODBBx/kqquuKpefn0oYRUqBE0lp/OOz33hz5S6Sz7CW8nmVQunVPIarm8VwRZOqVAoLPkdRioiUDfv27WPEiBHMnTvXV9arVy+mT59eLpNFUMIoEvC+3nyIu977lV3Hc58CJ8hh6N6oCte2iOGa5jE0j6lQbj/QRESKwlrLm2++yQMPPMDx48cBqFKlClOnTmXgwIHl+rNVCaNIgEpISWfMZ7/x6o/bTzsW4XJyeeOq9Du/Jje2rq41lEVEiigjI4MbbriBzz//3Fc2YMAAXnrpJWJiYvwYWWBQwigSYKy1zFu/nwc+2XjaSOcrGlfl6eua0652Rc2RKCJSjBwOB+3atePzzz+nVq1aTJs2jZtuusnfYQUMJYwiAcJayy+7T/Dw57+xcPPhbMfCXU6eua4Fwy+uj8NRfrtERESKU1JSEmFhYb79Rx55BPDMsVipUiV/hRWQlDCK+Nm6fXG8/+te3l+zl82HEk47fkn9aN78y4U0rhrhh+hERMqetLQ0nnnmGV599VV++eUXatSoAUBISAgTJkzwc3SBSQmjiJ+s3xfH6E82smDzoVyPx1RwMem6FtzR4Ty1KoqIFJNVq1YxePBg1q5dC8DIkSN5//33/RxV4FPCKHKObT2SwDPf/MnMZTvIyGWe7bBgB8Mvrs+jVzaloqbEEREpFklJSTz++ONMmTIFt9sNQMuWLRk9erSfIysdlDCKlCB3hmXX8ST+OJzAH4cTmL/xAF9sOojNkSiGBTu4rkV1bmlbi2ubxxARol9NEZHi8t1333H33XezZcsWAIKDg/nnP//Jww8/TEhIiJ+jKx30rSRSTI4npfHBmr2s3x/vSxC3HU3Md7m+YKfh/q4NGduziSbYFhEpZsnJyYwePZpp06b5yjp27Mjs2bOzrQstZ6aEUaQI0twZ/LT9KB+u2ce/Vu4iIdVdoHpBDsPNF9TiiWuaaTCLiEgJcblcrFu3DoCwsDAmTpzI/fffj9Pp9HNkpY8SRpFCOhCfwpebDvLZbwdY8PshTiSnF6heWLCDptUq0Pf8mgzpXJeaUaElHKmISPnmcDiYNWsWo0aN4qWXXqJx48b+DqnUUsIocgYZGZZVu0/w+W8H+Oy3g6zcdTzf88+vGUnzmAo0rhpB4yoRnm3VCGpGhZTrZaVEREqStZb333+f9957jw8//BCHw7O4QdOmTbOt3iJnRwmjSA7WWpZuP8bmQwl8u/UIX2w6yIH4lHzrVA4P5sZWNbjv0vq0q6PJXkVEzqW9e/cyfPhwPvnkEwBee+01RowY4eeoyhYljCJZrN0bx5AP1rBiZ/6tiABta0VxXcvqXNs8hovqRePUXIkiIueUtZbZs2fz4IMPcuLECQCqVq1K9erV/RxZ2aOEUcq93w+e5IM1npVW1u2Lz/O8CJeTK5tW49oWMVzbIobaFcPyPFdERErW1q1bGTJkCIsXL/aVDRw4kKlTp1K1alU/RlY2KWGUcmvR5kOM/WJTvq2JzWMqcE3zalzbvDrdGlUmJEgj60RE/MntdvPSSy8xduxYkpKSAKhduzavv/46119/vZ+jK7uUMEq5czwpjUHvrObTjQdyPe5yOujdugYv92lNTKQmdBURCSRHjx5lwoQJvmRx2LBhTJ48maioKD9HVrYpYZRyZX9cMtfMXM6avXHZyl1OB1c3q8bNF9TkxlY1tCSfiEiAqlatGi+++CITJkxg1qziZEarAAAgAElEQVRZXHbZZf4OqVxQwijlxrYjiVw1Yxl/HE7wlVUOD+aRnk0Y3KmukkQRkQC0cuVKVq1axbBhw3xlgwYN4pZbbiE0VPPZnitKGKVcWPLHYfr/+2eOJKb5yjrXi+bT2E5UiXD5MTIREclNYmIijz76KC+88AIOh4POnTvTtm1bAIwxShbPMYe/AxApSdZaXvlhGz2nL8uWLF7ZtCqL7umsZFFEJAAtWbKENm3a8Nxzz5GRkYExhl9++cXfYZVramGUMikxNZ33ft3L9KU7WJ5jFPSg9nWYeUsbjXgWEQkwJ06c4O9//zszZszwlV100UXMnj2bVq1a+TEyUcIoZcqxxFTGL9jMv1fuOm2NZ4eB525sxf1dG2iJPhGRAPPpp58ybNgw9uzZA0B4eDhPPvkk9913H06n/sD3NyWMUmacTEmnx+tLWb0n7rRjVSNczLm9HT2bVvNDZCIikp+1a9dyww03+PZ79OjBjBkzaNiwoR+jkqz0DKOUCe4My6B3Vp+WLF5QK4pp/c7nz39eoWRRRCRAtWnThjvvvJOKFSsye/ZsFi5cqGQxwKiFUUqtjAzLj9uP8t6ve/lw7T4OxKf4jrWrU5Fpfc+nU91K6n4WEQkwu3fvZv/+/XTo0MFX9vzzz/Pkk09Sq1YtP0YmeVHCKKWKtZZlO47x3q97+WDNPvbGJZ92Tp2KoXwa24maUZpyQUQkkGRkZDBz5kweeughqlSpwvr164mIiAAgOjqa6OhoP0coeVHCKKVGSrqb299ezYdr9+V5TsMq4cy9s6OSRRGRALNlyxaGDBnCt99+C0B8fDxz587l9ttv93NkUhBKGKVUSEpz0/fNlXy56dBpxyqHB9P3/JoMaFuL7o2qEOTUo7kiIoEiPT2dF198kXHjxpGc7OkVOu+885g+fTq9evXyc3RSUEoYpVQY9M7qbMlixdAg+p5fk1va1qJHk6oEK0kUEQk4a9euJTY2lp9//tlXNmLECJ5++mkiIyP9GJkUlhJGCXhL/jjM/7J0QzesEs7iYV2oVzncj1GJiEh+Fi1aRK9evUhP98yJ26RJE2bPnk3Xrl39HJmcDTXLSEDbeyKZhz7d6NuvFBbMt/derGRRRCTAXXrppTRq1Ain08k//vEP1qxZo2SxFFMLowSkHUcTmfzNH7yxYhcp6Rm+8jGXN6JOpTA/RiYiIrlJSEggJSWFypUrAxAaGsp//vMfHA4H7du393N0UlRKGCWgHEtM5aH5v/Hvn3eRnmGzHasXHcZ9lzbwU2QiIpKXr7/+miFDhtCpUyfeffddX3nHjh39GJUUJ3VJS8DYfOgknV/6gdkrdmZLFo2B/m1q8u29FxMRor9xREQCxfHjxxkyZAg9e/Zk27ZtvPfeeyxevNjfYUkJ0LevBISvNx+i/1urOJ6U5itzGBjYrjYPX9GEljU0mk5EJJDMmzeP4cOHs2+fZ1BiREQETz/9NJdddpmfI5OSoIRR/G760u2M+Gg97iytit0bVWHmLRfQuGqE/wITEZHTHDx4kJEjR/Lee+/5yq688kpmzJhB/fr1/ReYlCgljOI3Kelu7v94A9OX7shWPqRzXV7pcz6uID0xISISSL766isGDhzI0aNHAahUqRIvvPACd9xxB8YYP0cnJUkJo/jFrmNJ9H/rZ1bsPO4rcxh4/sZWjOzaQB88IiIBqF69epw8eRKAvn378uqrr1KjRg0/RyXnghJGOecWbznMrf9dxaGTqb6yiqFBzLm9Hb1aVPdjZCIiklVGhmdaM4fD0+PTvHlznn/+eWrUqEG/fv38GZqcYwHX52eMedAYs8sYc9AYM8EYU+AYjTFXGGNWGWMSjTHbjTFjjZqqAkZcchrjvtjEldOXZksWz68Zyc9/66ZkUUQkgGzevJnLL7+cGTNmZCsfMWKEksVyKKBaGI0xo4BngMeBn4GpwElgcgHq1gD+B7wAjAI6ApOAE8ArJRSyFEBSmptXf9jOpMVbOJKYlu3YwAtrM+PmNpouR0QkQKSnp/P888/z2GOPkZyczOrVq7nuuus477zz/B2a+FHAfEsbY1zAY8Dr1trHvWWJwFxjzHPW2vQzXOI6YK+19gnv/vfGmHZAb5Qw+s23fx5m0Dur2XU8OVt5kMPw/I2t+Oul9fW8oohIgFizZg2DBw/ml19+AcAYw5133kmlSpX8HJn4W8AkjEAHoBLwdmaBtfYbbzLREVh6hvpVOb2L3QUk53KulLB0dwYTF21hwsLN5Fiwhe6NqvDsDS3pcJ4+gEREAkFycjITJ05k8uTJpKd72meaNWvG7NmzueSSS/wcnQSCQEoYa3m3a3OU7wKacOaE8WvgKWPMvcBbQFfgRuDO/CoZY2KAajmKGxUgXsnD7uNJ3Pb2L3y39Wi28k51K/Fkr+b0aFJVrYoiIgFi6dKlDB48mE2bNgHgdDoZM2YM48aNIzQ01M/RSaAIpIQxDHBba+NzlCdxekJ3Gmvtz8aYx4BXvT8Az1lr382nGsC9eLrCpRjM37CfO9/9laNZnlUMdhqevb6lpssREQlAmzZt8iWLF154IW+88QZt27b1c1QSaAIpYUwBcntOMRVPMpkvY0xHYAyeQS8/AZ2B+40xh621k/KpOg34IEdZI2BeQYIWj9T0DMZ8tpEXv9uWrbxx1Qjevb0d7dX9LCISkO68807mzp3LxRdfzOjRowkODvZ3SBKAAilhPAiEGGOqWGuPZCmvDCQUoP444A1r7QPe/Q+NMfHAeGPMq7m0XAJgrT3ovbePWsEKJzU9g5vf+plPNhzIVn5bu9q81q8NkaGB9M9MRKT8OnbsGA899BAjR46kTZs2gOc7b968efruk3wF0jyMq/G0JvqerjXGRAJNgb0FqN8U+DNH2VogBKhTTDFKDqnpGdySI1kMdzl589a2/GfghUoWRUQCxEcffUTLli2ZPXs2sbGxvsEtoIYSObOASRittSeAhcCDxhint3gEYIDFBbjEYTwjrbO6HsgA9hdXnHJKcpqbAf9ZxbwsyWLd6DB+HtWVOzqepw8gEZEAsH//fvr370+/fv3Yv9/zdVilShXi43PteBPJVaA1/zwG/AAsM8ZsA/oDL1lrDxljqgANgNXWWncudecBk40xYcAfQFvgGuAta+2xcxN++RGXnMZNb6xkyZ+nnh44r1IoS4ZfTIMq4X6MTEREAKy1vPXWW/ztb3/j2DHP12DlypV54YUXGDRokP6ol0IJqITRWrvKGNMJmAA0xDOI5Xnv4RuAfwHRwPFcqj+HZ87Fe/BM4p0E/BsYWcJhlzsH4lPoNXMZq/fE+crqVAxlyb1KFkVEAsGOHTu45557+Oqrr3xlN998My+//DLVq2sZVim8gEoYAay16/CszpKz/E3gzXzqZQAve3+kBLgzLB+v38ffP/2NrUcSfeXNqkWw4J7O1I1WsigiEggmTpzoSxZr1KjBtGnT6NOnj5+jktIs4BJGCUy7jiVx/ewVrN0Xl62843mV+PzuTlStEOKnyEREJKdJkybxySefcP311zNlyhSio6P9HZKUckoY5YyOJ6XRa9ZyNuzP/oD0lU2r8tGdHakQon9GIiL+kpaWxuuvv05sbCzh4Z6enipVqrBx40aqVKni5+ikrNA3veQrKc1Nn3+tzJYs1osO48HujbinSz2CnQEz0F5EpNxZvXo1sbGxrF69mh07djBlyhTfMSWLUpyUMEqejiel0e/Nn7ONhL6kfjQLh3UhLNiZT00RESlJycnJPPHEEzzzzDO43Z6JQxYsWEBKSgohIXpESIqfEkY5zcmUdKZ+v5UpS7ZyPOnUmtDNqkUwb3AnJYsiIn70ww8/EBsby+bNmwEICgri4YcfZuzYsUoWpcQoYZRs4pPTueSVH1i3L/vzis2qRfD18C5UiXD5KTIRkfItPj6ehx9+mFdffdVX1r59e9544w3fMn8iJUUJo2Rz39x1pyWLVzWtxlsDL6R6pP5yFRHxl759+7Jo0SIAQkNDeeKJJ/jb3/5GUJC+yqXk6V+Z+Ly5Yhf//nm3b/+CWlFM7d2KyxpV9WNUIiICMG7cOBYtWkS3bt2YNWsWTZo08XdIUo4oYRQAPlq7j9j3f/XtVwoLZv7gTpwXHebHqEREyidrLcuXL6dz586+sm7duvHtt99y6aWX4nBohgo5t/QvTpi3fj+3/ncVGdazbwzMvuUCJYsiIn6wb98++vXrR5cuXfj000+zHevWrZuSRfELtTCWQ8lpbj7ZcIDvth7hu61HTntm8fV+bejbpqafohMRKZ+stbz55ps88MADHD9+HIBHHnmE6667DmOMn6OT8k4JYzmT7s7g0ld+ZNXuE7ken3JDS4Z2qXeOoxIRKd+2bdvG0KFDfYNaAG699VamTp2qZFECgtq1y5kFmw/lmiyGBDl47saWjO7eyA9RiYiUT263m5deeonWrVv7ksVatWoxb9485syZQ0xMjJ8jFPEocgujMaYZcC9wAfAgkAY8Aky01q4p6vWl+KS7M5j63TbfflRoEDe3qUXXhpW5vmV1zbEoInIOZWRk0LNnT5YsWeIrGzJkCM888wyVKlXyX2AiuShSwmiMaQ0sBSIAC0QBR4DewDXGmK7W2l/zuYScI6npGdz+zi8s2HzIV3bvxfV5+roWfoxKRKT8cjgcXHHFFSxZsoSGDRsyc+ZMrrjiCn+HJZKrorYwTgb2AHcCPwFYa9cYY+oBC4GJwPVFvIcUUXKam5vfWsWnGw/4yqpVcDGyawM/RiUiUv6cOHGCihUr+vbHjBmDy+Xir3/9KxEREX6MTCR/RX2GsRPwOrAla6G1di/wGtA5t0py7iSkpHP97BXZksWaUSF8e+/F1IwK9WNkIiLlR1JSEmPGjKFhw4bs2bPHV+5yuRgzZoySRQl4RU0YXUBiHsfCgOAiXl+KIC45jatnLOPrLYd9ZfWiw/h+xCW0qB7px8hERMqPb7/9lgsuuIBnnnmGo0ePMnLkSH+HJFJoRU0YlwO3AtnG/BtjooChwLIiXl/OUro7g/7//pkftx/zlTWpGsH3Iy6hUVX9JSsiUtLi4uIYPnw43bt3Z8sWT0dcp06dePzxx/0cmUjhFfUZxseBb4Cv8Ax6uc0Y0xfPoJdqwB1FvL6cpTGf/cbCzadaFlvXiGThPZ2poW5oEZES99lnnzFs2DB2794NQFhYGBMnTuT+++/H6XT6OTqRwitSwmit/dEY0xt4GU8r413eQ9uA3tban4oYn5yFQydTmPr9qelzGlYJ55vhXahaIcSPUYmIlH2JiYkMHTqUt99+21d2+eWXM3PmTBo10jy3UnoVeR5Ga+3nwOfGmCZ4WhUPW2s3FzkyOWsfrNmHO3NhaGDunR2VLIqInANhYWHs27cPgKioKKZMmcLdd9+t1Vqk1Cu2lV6stVustT8pWfS/d37Z7Xt9cf1o2tSK8mM0IiJlm7Wn/kA3xjBz5kxuueUWNm7cyJAhQ5QsSplQYksDGmPuN8bcXVLXl9OlpLt5aP7GbANdBl5Y248RiYiUXdZaZs6cSa9evXC73b7yhg0b8t5771G7tj5/pewoUsJojNlqjLkxj8PReAbFyDnw24F4Ok/9gSlL/vSVhQY5uPmCWn6MSkSkbPrzzz/p0aMHQ4cO5auvvuLll1/2d0giJaqozzDWByrkcWwbnmcapYR9v/UI181aQXxKuq8sNMjBv25tS0yknl0UESkubrebqVOn8sgjj5CUlARA7dq1adq0qZ8jEylZRR70gmc6ndy0BPYVw/UlHwt+P0jvf60kKS3DV9amZhTv3N6OVjU0ObeISHFZv349sbGxrFixwlc2bNgwJk+eTFSUnhWXsq3QCaMx5g6yz6/4iDFmSI7TKgJtgUlFiE3O4OvNh7hh9kpS3aeSxXsvrs/zN7UkJEjzfImIFIfU1FSefvppnnzySdLS0gBo3Lgxs2bN4rLLLvNzdCLnxtm0MFYCGmTZrwaE5zjnMJ7nF586y7jkDKy1/HXu+mzJ4qNXNmX81U01Ik9EpBjFxcXxyiuvkJaWhsPhYPTo0YwfP57w8JxffSJlV6ETRmvtVGAqgDEmAxhlrX2nuAOT/H2/9SibDp707Y+/qimPXd3MjxGJiJRNVatW5eWXX+app57ijTfeoEOHDv4OSeScK7FpdaRkzVi2w/e6SngwY65o7MdoRETKjiVLlvDCCy9kKxswYACrVq1SsijlVnGsJb22OAKRglu+4xjv/brXt39Hx/MIDdYziyIiRXHixAn+/ve/M2PGDBwOB127dvUliMYYgoOD/RyhiP8UqYXRWvu4tXZ9cQUjZ7Y/LpkB/1lFunfpP6fDcE+Xen6OSkSkdJs/fz4tW7ZkxowZAISGhrJlyxY/RyUSOIo8rY4xJgq4GIjJcSgMuN5ae0NR7yEe6e4Mrp+9gh3HknxlE65pRtNqeU2FKSIi+Tl06BD3338/c+bM8ZX17NmTGTNm0KBBg3xqipQvRUoYjTFtgS/IniwaTs3N6D6tkpy1eRv2s2r3Cd/+tS1iGHO5nl0UESksay1z5sxh5MiRHDlyBICKFSvy/PPPc9ddd2m2CZEcijroZRIQB1wPbAH+C3QEZgFpQPciXl+ymL701ECXOhVDeX9QexwOfaiJiBTWmjVruO2223zJYu/evdm4cSODBw9WsiiSi6ImjO2BV6y1XwDvAE2staustfcAq4C/FjVA8fjjcAILNx/27Y+4pD4RIcWxUI+ISPnTtm1bhg4dSkxMDB988AEfffQRtWrV8ndYIgGrqAljEJDqfb0KON+c+tPsPeCqIl5fvGZmmUYn2Gm4q1NdP0YjIlK6bNmyhZ9++ilb2bPPPsvGjRvp37+/WhVFzqCoCeM6INYYUxVYhmfFl87eYy5AcxAUg5R0N2+s2OXb73t+TapHhvgxIhGR0iE9PZ0pU6bQpk0bbr31VuLi4nzHoqKiqFKlih+jEyk9ipowPgVcCLxurT0C/AB8aIx5CxgHrMivshTM7OW7OJyQ6tvXNDoiIme2du1aunTpwkMPPURycjK7du3iiy++8HdYIqVSUedh/BJPt/MSb9FQIB64HTgBPFCU6wscT0rjsa9+9+23rhFJ90b6i1hEJC8pKSk8+uijtG/fnp9//hmAJk2a8N133zFgwAA/RydSOhV51IS19hvgG+/r34HmxpjK1tqjRb22wFOLtmRrXXz2hpZ61kZEJA/Lli0jNjaWjRs3AuB0OnnooYd49NFHCQsL83N0IqVXiQyzzUwWjTFNrLWaKv8sbTl0kqnfb/PtX92sGtc0zzk/uoiIAHz55Zdce+21WOuZCviCCy5g9uzZtG/f3s+RiZR+he6SNsYMNsZsNMYkGmO2GmPGG2McOc5pboz5L7Cx2CItZ9LcGdz29mpS3RkAOAxMuaGln6MSEQlcV1xxBS1btsTlcvHkk0+ycuVKJYsixaRQLYzGmF54JuVOBjYAtfAMbokAHjLGtPTu98eTjM4r1mjLkScWbGblruO+/b9e2oDWNaP8GJGISGA5fvw4aWlpVKtWDQCXy8Xbb7+Ny+WiRYsWfo5OpGwpbAvj34B9QHNrbUfgPDwTdg83xnwArAVuwZMoXmit7VucwZYXmw7EM2nxH779ltUrMOk6ffiJiGT6+OOPadmyJffcc4+vCxo83dBKFkWKX2ETxvOBmdbanQDW2gw8ywOGA32Bj4ALrLX9rbVrizXScsJay31z15Oe4fkAdDoMb9/WjrBgp58jExHxvwMHDnDLLbfQp08f9u3bx9y5c1myZIm/wxIp8wqbMFYHtuUo2+/d/sVae4u1dn3Rwyq/Ply7j0VbTi0BeN+l9Wlbu6IfIxIR8T9rLf/5z39o2bIlH3zwAQDR0dG8+eabdO/e3b/BiZQDZzNKOi2P8kNFCUQ8H4j/+Ow33371yBDGX9XMjxGJiPjfzp07ueeee/jyyy99Zf369eOVV16hRo0afoxMpPw4m4RxhDHm+iz7LsACjxhjhuQ411prbzvr6MqZ3w6cZOuRRN/+pGtbUDFMqyuKSPk1b948br/9dk6ePAlA9erVefXVV+nXr5+fIxMpX84mYezi/cnp8lzKLKCEsYC++eNUV3SQw9D/gpp+jEZExP9atmxJeno6AHfddRfPPfcc0dHRfo5KpPwpbMLYoESiEAAWbj7Vq39R3UpUCCmRedVFRAJWeno6xhicTs9AvyZNmvDqq69Sp04drrrqKj9HJ1J+FSojsdbuKKlAyruN++P59LeDvv0rmlT1YzQiIufer7/+SmxsLHfeeSf33Xefr3zw4MF+jEpE4CxWepGSMXP5DtzeqXSCHIa/XFjbzxGJiJwbycnJjB07lg4dOvDLL7/w8MMPs337dn+HJSJZqM8zAFhr+Xj9ft/+wHa1aVE90o8RiYicGz/99BOxsbFs2rQJAKfTyahRozT6WSTAKGEMAGv3xbH9aJJvv+/5GuwiImXbyZMn+ec//8krr7ziW6nlwgsv5I033qBt27Z+jk5EclLCGAA+XneqdTEs2MGVTfX8ooiUXYsXL2bw4MHs2OF5LD4kJITHH3+c0aNHExSkryWRQKTfTD9LTnMzY9lO3/7VzWIId+l/i4iUXfv27fMli5deeimzZs2iWTMtUiASyJSZ+Nns5TvZG5fs27+r43l+jEZEpOQNHDiQ+fPn061bN4YNG4bDofGXIoEu4H5LjTEPGmN2GWMOGmMmGGMKHaMxprUxJs0Y078kYiwuKelunl78h2//wtpR3NCquh8jEhEpXvv372fAgAH88ssvvjJjDO+++y733nuvkkWRUiKgflONMaOAZ4BZwF3AX4CHCnkNA0wHvrPWfljsQRajN1bsYs+JU62Lj13VDE/4IiKlm7WWf//737Rs2ZL333+f2NhY0tLS/B2WiJylgOmSNsa4gMeA1621j3vLEoG5xpjnrLXpBbzUEKATENDD7Ky1vPzDNt9+21pR3KjWRREpA7Zv384999zDggULfGVNmzYlMTGRihUr+jEyETlbgdTC2AGoBLydWWCt/cb7smNBLmCMiQEmAa9YazcUe4TF6IdtR/ntwEnf/oPdG6l1UURKtYyMDF5++WVat27tSxZr1qzJ3Llzee+995QsipRiRU4YjTHNjDFTjTFLjDEdjDEXGGM+MMZcUMhL1fJu1+Yo3wU0KeA1ngWigSrGmP8aY4ad6RlIY0yMMaZV1h+gUaEiPwszlp1aZbFyeDD92mjuRREpvTZt2kS3bt0YOXIkCQkJAMTGxrJx40Z69+7t5+hEpKiK1CVtjGkNLAUiAAtEAUeA3sA1xpiu1tpfC3i5MMBtrY3PUZ4EVCtALOcDgwA3UB8IB27zxtHHZs4Me7p78XSFnzMJKel8sGafb/+ODucRGuw8lyGIiBSrF154gR9//BGABg0aMHPmTHr06OHnqESkuBS1hXEysAe4GDAA1to1QD1gJzCxENdKAXJ7TjEVTzJ5JkO9MfS31naz1nYARgI3AVfmU28a0DrHz02FiLvQVuw6Tkp6hm//jo51SvJ2IiIlbvLkydSpU4dRo0axbt06JYsiZUxRE8ZOwOvAlqyF1tq9wGtA50Jc6yAQYoypkqO8MpBQgPpNgS3W2o+zlM0CMoAL86pkrT1ord2Q9Qf4sxBxF9qc1Xt8ryuFBXN+jaiSvJ2ISLFKSkriqaeeIj7+VIdQpUqV2LhxIy+88AIRERF+jE5ESkJRE0YXkJjHsTAguBDXWo2nNfGSzAJjTCSeRHBvAeonAFtzlKXiSRhTChFHiVq/L45Zy0+t7NKjSVUcDg12EZHS4fvvv6dt27aMHTuWsWPHZjsWGRnpp6hEpKQVNWFcDtyKtzs6kzEmCk8X8bKCXshaewJYCDxojMl8oG+E99qLC3CJlUBLY0zWJPUSPM9pLi9oHCVt+tIdZD5NGRLk4Mlezf0bkIhIAcTHxzNixAi6devG5s2bAVi+fDkpKQHz97iIlKCiJoyPA5cCX+EZ9HKbMeYVYCNQ13u8MB7DM4XOMmPM+8BTwMvW2kPGmCreUdh5jQ6ZhWegy7vGmB7GmNuA/wDfW2uXFvqdlYCkNDf//eVUd/RfLqxNs5gKfoxIROTMvvjiC1q1asW0adMACA0N5ZlnnuHHH38kJCTEz9GJyLlQpITRWvsjnhHRlfC0BN6FZ9RxCtDbWvtTIa+3Cs9zkXuAhsAYYLT38A14WhFz7fOw1h7Ck7w6gPeBV73nB8zygB+u2cvxpFMrHQy5qK4foxERyd+RI0f4v//7P6699lp27doFQLdu3Vi7di0PPfQQQUEBs/aDiJSwIv+2W2s/Bz43xjTBM/3NYWvt5iJcbx2eJDRn+ZvAm2eouwnoc7b3Lmkzszy72LJ6BbrUj/ZjNCIi+evfvz9LliwBPM8nPvvsswwZMkTrP4uUQ8X2W2+t3WKt/akoyWJZtulAPN9vPerbH9K5nlZ2EZGA9tRTT2GM4brrrmPjxo3cc889ShZFyqki/eYbY1YYY/5qjKlaXAGVVVlHRrucDga119yLIhI4rLUsWrQoW1mXLl1YuXIl8+fPp04dfWaJlGdF/VMxHHgJ2GOMmWeM6W+M0RPQuXgny9yL/drUpEqEy4/RiIicsm3bNq666iquvPJKPvroo2zH2rdvr94QESnyoJfWQBvgOTwrpLwP7DfGzDDGdC2G+MqEo4mp7Is7NfXELRdo3WgR8T+3283UqVNp3bq1r3XxqaeeIu+VVEWkvCrywyjW2vXW2n9aaxvhWSLwLeB6YIkxJudE2uXS1iPZ5zZvWk1T6YiIf23cuJGuXbsyatQoEhM9n1FDhw7l66+/VouiiJymWOdEsNYuM8a4gAhgMJ41pcu9Pw9nX9mwQZVwP0UiIuVdWloakydPZsKECaSmpgLQqFEjZs6cyeWXX+7n6EQkUBVLwmiMaQ/8BRgA1AIOAVPxTJxd7m09eqqFsa/whawAACAASURBVFZUKGHBec09LiJScjIyMujatSvLl3sWv3I4HPztb3/jiSeeIDxcf8iKSN6KlDAaYx7Hkyg2ApKBT/AkiV9Za91FD69s2Hgg3ve6UVV9KIuIfzgcDvr06cPy5ctp3bo1s2fPplOnTv4OS0RKgaK2MD4CfAdMAj6w1saf4fxyJ82dwee/HfTtt6td0Y/RiEh5c/DgQWJiYnz7o0ePJjIykrvvvhuXS7M1iEjBFHXQSwNr7eXW2jeULOZu/oYDHE08tRxgvzYaIS0iJS8uLo7hw4fTuHFjdu48NQ9sUFAQ9957r5JFESmUok6rs/PMZ5Vf1lqeXfKnb79udBgX16/sx4hEpDz47LPPaNWqFa+//jrx8fGMGjXK3yGJSCmnNZ5K0I/bjrJsxzHf/gPdGuJ0aLoKESkZhw8f5vbbb+f6669n9+7dAFx++eU8++yzfo5MREo7JYwl6L1f9/peR4cFE3tRXT9GIyJllbWWd999lxYtWvx/e/cdH1WV/nH886QKUgWxgBQFlAALKkhxLStr773xExWQFeyioosiiwi6iIqitKDuCqtrV5TFiiiCICJVEQQVK70GCEnO7487mUwiZCaZmdyZ5Pt+ve4rc88t88zcmeTJOfecw6RJkwCoVasW48eP54MPPuCwww7zOUIRSXYxHYdRipv30+bg43NaH0CNTL3dIhJb27dv54orruDNN98Mlp199tk8/fTTNGzY0MfIRKQyKVMGY2YTgbHOuc9D1kvjnHM9yxtcMsvLL+CrX4oSxqMb1fExGhGprKpXrx4cgHv//ffniSee4JJLLtFsLSISU2Wt8roaeB/4PGS9NA6okgnjkt+3smN3QXD9qEYaTkdEYsM5F0wIzYyxY8cyePBgHnroIerXr+9zdCJSGZXpHkbnXIpzbnKJ9dKWKjulyZSlvwcfZ6alcGTDWj5GIyKVQX5+PiNHjuSEE04gLy8vWN64cWOys7OVLIpI3KjTS5xMW7Y2+PivLepTPUP3L4pI+S1evJiuXbty++2388knnzBy5Ei/QxKRKkQJYxzkFzi+DOnw0q2F/usXkfLJzc1l8ODBHHXUUcyZMweA5s2b06lTJ58jE5GqRNVecbB87Ta25xZNpa0OLyJSHnPmzKFnz54sXrwY8OaC7t+/P/fffz/VqlXzOToRqUqUMMbB5Pk/F1tvr/sXRaQMcnJyuO+++3j00UcpKPA6z7Vt25aJEyfSoUMHn6MTkaoobk3SZnazmfWK1/kT1eqNOxj2wYrgeodDalNrn3QfIxKRZLNz506ef/55CgoKyMjIYMiQIXzxxRdKFkXEN1EljGa20szO2cvmusDgaM6fjN5fvpa8Ahdcf+zcNj5GIyLJaL/99mP06NF07tyZ+fPnM3DgQDIyMvwOS0SqsGhrGJsCNfaybRWwf5TnTzor1m0PPm5ef1+Obbafj9GISDJ48803GTp0aLGyCy+8kJkzZ5KVleVTVCIiRWJxD6PbS3kW8GsMzp9UPlqxPvj4iAZ7y6VFRGDNmjXcdNNNvPjii5gZ3bp1o3PnzsHtKSkayEJEEkOZE0Yz6wH0CCkaaGa9S+xWG2gPDI8itqTz48YcZv2wMbh+RqsGPkYjIonKOcfkyZO5+eabWb/e+yezdu3a/PprlfsfW0SSRHlqGOsAzULW9weql9hnHd79iw+WM66k9NaSotldUgwubHuQj9GISCJavXo1119/PW+//Xaw7Pzzz2f06NEcdJB+Z4hIYipzwuicexx4HMDMCoBbQqcLrMo+XlnUHN2pcV0a1Mz0MRoRSSQFBQWMGzeOO++8k61btwLQoEEDRo8ezYUXXhicG1pEJBFpHMYYycnN452v1wTXT2xez8doRCTRLFy4kL59++Kcd9v3VVddxciRI6lXT78rRCTxRXtH9WBgYSwCSXbTlq0tNrvLxX862MdoRCTRtG/fnn79+tG4cWOmTp3Kc889p2RRRJJGVAmjc26wc25xrIJJZrO+L+rs0qRuNc3uIlLFLVy4kI8++qhY2bBhw1i8eDGnnXaaT1GJiJSPxmyIga0784pNB3hM4zq6H0mkitq1axf33XcfRx99NN27d2fTpk3BbTVq1KBmzZo+RiciUj5KGGNg2IfL+XnzzuD6KS2r3HjlIgLMnj2bo446iiFDhpCXl8fvv//+h1pGEZFkpIQxBl5dWDR2WqfGdbi64yE+RiMiFW379u3ceuutdO3alaVLlwLePYtz5szh/PPP9zk6EZHolamXtJlNBMY65z4PWS+Nc871LG9wyeDnzTtYtrZoOsBbjj+UtFTl4SJVxQcffEDv3r1ZtWoVAJmZmQwaNIj+/fuTnp7uc3QiIrFR1mF1rgbeBz4PWS+NAyp1wvjB8nXF1k9qXt+nSESkor399tucddZZwfVjjz2WCRMmcMQRR/gYlYhI7JUpYXTOpZS2XhW9/21Rwving2ppsG6RKuSUU06hXbt2rFixguHDh9O3b1/N/ywilZIG7o6Cc65YDWO3FqpdFKnMfv/9dwoKCoJT+KWnpzN58mT23XdfmjRp4nN0IiLxo3+Fo7BszTZ+2VLUO/qvLZUwilRGzjn+9a9/0apVK3r37h2crQUgKytLyaKIVHpKGKPw4YqiuaPTUozjD9WsDSKVzY8//sgZZ5xBjx492LhxI2+//TYzZszwOywRkQqlhDEKC37ZHHzc7uBa1MhUC79IZVFQUMDo0aNp3bo1//vf/wA44IADeOWVVzjhhBN8jk5EpGJFneGYWUOgvnNuQWD9CuAY4EPn3JvRnj9R7c4v4LXFvwXX2x9c28doRCSWli1bRq9evfj000+DZddccw2PPPIIdevW9TEyERF/RFXDaGbHAl8DfQLrtwHPAzcBr5nZlVFHmKD+980a1m7LDa5fcVRDH6MRkVh56aWXaNeuXTBZbNKkCdOmTWPixIlKFkWkyoq2SfoBYCnwUGC9H/AMUBeYDtwa5fkT1qQvi+aObly3GicepvsXRSqDDh06kJqaiplx0003sXjxYk455RS/wxIR8VW0TdLtgQHOuR/M7CCgGXCBc26zmb0AjIw6wgS0O7+Aqd+sCa5fcWRDUlLMx4hEpLx27txJeno6qampADRr1ozx48fTpEkTjj32WJ+jExFJDNHWMDqgMFM6EtgNLAmspwM793RQsvt01Qa27MwLrp+ddYCP0YhIec2cOZP27dvz2GOPFSu/4oorlCyKiISINmFcAlxjZm3xmqPnO+fyzCwFuAD4JtoAE9HMVRuCj/ernk6nJrqvSSSZbNu2jZtuuonjjjuOZcuWce+99wbnghYRkT+Ktkl6KPAW8BVebWNhJ5evgeYh65XKqg05wcdtD6pFqpqjRZLGu+++y3XXXccPP/wAQGZmJoMGDeKQQw7xOTIRkcQVVcLonPufmXUBjgdmOuc+D2z6DBjmnHsh2gAT0fcbdgQfN9uvuo+RiEikNmzYwO23386zzz4bLDvuuOOYMGECLVu29C8wEZEkEPU4jM65L4AvSpRdE+15E1loDaMSRpHEN2XKFHr16sXvv/8OQI0aNXj44Yfp06cPKSmav0BEJJyYTU1iZgbUBja70IlWK5m8/AJWbyqqYWy6XzUfoxGRSOTk5ASTxdNPP50xY8bQuHFjn6MSEUkesZjppREwAjgLqAbsNLO3gf7OuR+jPX+imf/zFvIKivLh5vX29TEaEYnExRdfzLvvvsuJJ57IlVdeiff/rYiIRCramV4aArOBi4GFwEvAfLwe0rMCyWSl8u63ReMv7puRSodD6vgYjYiU9P3333PGGWfw+eefB8vMjAkTJtC9e3cliyIi5RBtDeMwoBbwZ+fcrMJCM+sEvAs8CFwV5XMklHe+LkoYTzysHhlpuv9JJBHk5+fz1FNPcffdd7N9+3ZWr17NvHnzyMjI8Ds0EZGkF23CeAbwWGiyCOCc+9zMRgHXR3n+hPLTph189v3G4PqphzfwMRoRKfT111/Tq1cvPvvss2BZ586dyc3NVcIoIhID0VaPVQPW72XbusD2SmP5uu3F1s/SDC8ivtq9ezdDhw6lffv2wWSxWbNmvP/++4wfP54aNWr4HKGISOUQbcI4F+hjZsUSQzOrDlwX2F5p/LixqHd0WorRuG6lyodFksqXX35Jx44dGThwILm5uZgZt956K4sWLaJbt25+hyciUqlE2yQ9CHgfWGxm2cBPQCOgJ9AYb7rASuPHkOF0GtXZRzO8iPgoOzubBQsWAJCVlUV2djadO3f2OSoRkcop2plePjazy4GngQfwpgc0YANwpXNuetQRJpCV64sG7G5SVwN2i/hp2LBh/O9//6N79+7cc889ZGZm+h2SiEilFYuZXl42sylAV+BA4HfgM+fcjtKPTD7L124LPm5RX+MvilSUrVu38uCDDzJgwABq164NQK1atViyZAn77LOPz9GJiFR+5U4Yzaw2kOmcW+Oc2wl8GIuAzKw/cDOQCYwFBjnnCspxng54Y0Q2d859H4vYVoTUMDZXwihSIaZOnUqfPn1YvXo1GzduZMyYMcFtShZFRCpGmTu9mNm5ZrYUr9n5VzNbZ2b3m1nUAxKa2S3Aw8AE4BrgcuCOcpwnA5gIpEYbU6EtO3fz+9ZdwXXVMIrE1/r167nqqqs444wzWL16NQDLli0jNzfX58hERKqeMtUwmtnpwKt4yeL/gBygHXAv3jzSt5Y3kECSNwgY45wbHCjLAV4zs0ecc3llON1AoEl5Y9mT79blFFtXDaNIfDjnePnll7nhhhtYs8YbKL9mzZr885//pHfv3qSkaLB8EZGKVtbfvH8HlgMtnXNnOucuBo4Angf6Bpqpy6sDUAeYVFjgnPso8LBjpCcxs3bAXcDtUcTyByvWFx+DsXl9dXoRibVffvmFCy64gEsuuSSYLJ555pksXbqUPn36KFkUEfFJWX/7Hgk865wLTncSuL/wMSAdyIoiloMDPxeWKF8NtIjkBGaWBjyD1xz9foTHNDCz1qELcFjJ/ZavLUoYG9beh+oZUfcXEpESLr/8cl5//XUA6tWrx6RJk3jrrbdo1KjSTUsvIpJUypowVsMba7Gkn0K2l1c1IN85t7VE+Q5g/wjPcRdeLWVZ7nvsCywusbxRcqfVIWMwNttPtYsi8TBixAhSUlK47LLL+Prrr7niiisw03inIiJ+K081Wf4eygp7MbsoYtkF7Ok+xVwiSETNLAvv3sXTnHPbzKx+hM/7FPBSibLDKJE07sor6qhdIzNmfWlEqqz8/Hzeeustzj333GBS2LFjRxYvXkyrVq18jk5EREKVJ2F80MxK1uCl4iWLY81sW0i5c84dHeF51wCZZlbPORc6P/V+wPa9HAOAmaXiNUOPd859HOHzFQa4JvDcoef7w36hCWNGqu6jEonG0qVL6dmzJ7Nnz+aFF17g0ksvDW5TsigiknjKk/k0AdqXWNrizfDSfA/bIjUfrzbx2MICM6sJtAR+CXPsIUAn4EYzc2bmgFWBbavMbHoZ4tijb9cV5cH1qmdEezqRKik3N5chQ4bQvn17Zs+eDcDjjz+Oc9E0ToiISLyVqYbRORe3qjXn3GYzew/ob2ZvO+fy8eaiNsIPCv4LXoecUAcDbwNnAt9GE1tObh5f/bwluN6pSZ1oTidSJc2dO5eePXuyaNEiAFJSUrjtttsYPHiw7lMUEUlwidbVdxDwKTDbzFYBFwGjnHNrzawe0AyYH0gmg5xzucBXoWVmtinwcGm0M718sXozeQVFNSBdm+4XzelEqpScnBwGDRrEyJEjKSjwbu1o06YN2dnZHHPMMT5HJyIikUiom/Gcc/OAY4CfgUMpPp7i2cBcoGZFx7Xw16LaxRqZqWQdUOEhiCSl/Px8unTpwogRIygoKCA9PZ3Bgwczb948JYsiIkkkoRJGAOfcIufcec65Ds65fxbWJjrnnnXOmXNuU7hzBPb/PrD/99HG9MuWncHHTetWJzVFzWcikUhNTaVHjx4AHHPMMXz55Zfcd999ZGToPmARkWSSaE3SCWn99qK5a/evoT90IqX58ccfady4cXD95ptvpn79+lx55ZWkpmpIKhGRZJRwNYyJaMfuoiF1qqfrD57Inqxbt47u3bvTqlUrVq5cGSxPTU3lqquuUrIoIpLElDBGYMfuoj421ZQwihTjnOOFF16gVatWTJo0iZycHPr37+93WCIiEkNqko5AaMK4T7pybJFCP//8M9dffz1vvfVWsOycc87hiSee8DEqERGJNWU/EVgbcg/jvhnKsUWcc4wfP56srKxgsrj//vvzwgsv8Prrr9OwYUOfIxQRkVhS9hNGfoHjy582B9fbHqghdaRq27ZtG+eccw4fffRRsKx79+48+uij1K8f6RTuIiKSTJQwhrFpx+5ig3a33L+Gj9GI+G/fffelRg3ve9CoUSPGjBnDmWee6XNUIiIST0oYw9iQk1tsvW71dJ8iEfFPXl4eaWnerwsz46mnnuLQQw/lH//4B7Vq1fI5OhERibeo72E0s8PN7HEzm25mHcysnZm9ZGbtYhGg39bn7C62Xn9fjcMoVUdubi73338/Xbt2Zffuou9Co0aNeOyxx5QsiohUEVEljGbWBvgCuBE4Dij863Ee8KmZtY8uPP+t2168hlEJo1QVc+bM4aijjmLw4MHMnTuXhx56yO+QRETEJ9HWMD6EN+9zV8AAnHMLgCbAj8ADUZ7fd+u2FSWMmWkp7JuhcRilcsvJyeH222+nS5cuLFmyBIB27dpx+umn+xyZiIj4JdqE8RhgDLA8tNA59wvwNNA5yvP7bsOOooRxv+rpmGkeaam8PvzwQ9q2bcvIkSMpKCggIyODBx54gLlz53L00Uf7HZ6IiPgk2k4vGUDOXrZVA5K+h0hok7RmeZHKasuWLdx+++1MmDAhWNalSxeys7Np1aqVj5GJiEgiiLaG8XPgMgLN0YXMrBZwHTA7yvP77rt1Rflw1gEag1EqJ+ccU6dOBaB69eo8/vjjfPLJJ0oWRUQEiD5hHAz8GZgGOOBKM3sSWAo0DmxPalt2FfUMrachdaSSql27Nk8//TQnn3wyixcv5qabbiI1VTXqIiLiiSphdM7NxOsRXQevlvEaoC+wCzjPOfdZ1BH6LCe3aB5pTQsolYFzjkmTJjFw4MBi5WeffTbTpk2jWbNmPkUmIiKJKuoMyDn3DvCOmbUA9gfWOee+jTqyBLFjd0HwcbV0Tb0tyW316tX87W9/45133gHg1FNP5bjjjgtuV6cuERHZk5hVmTnnllOit3RlkLO7qIaxuobUkSRVUFDA2LFjueuuu9i6dSsADRo0YNu2bT5HJiIiySCqhNHMPgyzi3POdYvmOfwW2iStXtKSjJYvX06vXr2YMWNGsOyqq65i5MiR1KtXz8fIREQkWURbw7gfXmeXQrXwBu1OAX4F1kR5ft9t2VnU6aVWpu5hlOSRl5fHyJEjGTRoEDt37gSgcePGjB07ltNOO83n6EREJJlElQE55/4w9Z+Z1QQGAD2AC6M5v9+cc2zamRdcr1NNvaQleSxatIgBAwbgnPc/Xb9+/Rg2bBg1a2p4KBERKZuY9+Jwzm11zv0dGAGMjPX5K9KO3fnkFxRVoCphlGRy5JFHcsstt9CyZUtmzJjBk08+qWRRRETKJZ7dfl8ETojj+eNuS0jtIihhlMQ2a9Yspk2bVqxs6NChLFiwoFhPaBERkbKKZ8J4MZAfdq8EtnWXEkZJfNu2beOWW27h2GOPpUePHmzYsCG4rVq1auyzzz4+RiciIpVBvHpJHwy0ACZHc36/bS1Rw1h7H3V6kcTy3nvvcd111/H9998DsGnTJj777DPOOussfwMTEZFKJdoM6FCK95IutA0YDQzcw7aksT23eMJYSwmjJIiNGzfSv39/Jk6cGCw79thjmTBhAkcccYSPkYmISGUUbS/ppjGKIyFtyy3eol5DUwNKAnjttdfo27cvv/32GwA1atRg+PDhXH/99aSkaDYiERGJPWVApdgekjDWzEwjJUXTpom/3njjDS644ILg+qmnnsrYsWNp0qSJj1GJiEhlp+qIUoQ2SdfUoN2SAM4880w6dOhA3bp1ee6555g6daqSRRERibtoO718Ckxzzg2JUTwJZfuu0BpGTQsoFe+HH34gJSWFQw45BIC0tDQmT55MzZo1OfDAA32OTkREqopoaxhrAJX2Dvvtu0MSRnV4kQpUUFDA6NGjadOmDb169QrO1gLQokULJYsiIlKhok0YnwPONLNasQgm0eTsLn4Po0hFWLZsGSeccAI33HAD27Zt491332XmzJl+hyUiIlVYtAnj48AnwDQzaxSDeBJKaJO0ekhLvO3evZvhw4fTrl07Pv30UwCaNm3Ku+++y5///GefoxMRkaos2ixoArAe+AuwzMzeAnJCtjvnXM8on8M3qmGUijJ//nx69uzJ/PnzATAzbrzxRoYOHUqNGjV8jk5ERKq6aLOgq0usX1Ji3QHJmzCG9JKuoU4vEieTJk2iR48e5Od7/6AcccQRZGdn07VrV58jExER8UTVJO2cSwmzJHWWtT5nd/Cx5pGWeDn++OOpVq0aaWlpDBw4kPnz5ytZFBGRhKJ21lKs3rQD6niPD61X3d9gpNLYtm0bmZmZpKd7/4QccsghPPvsszRv3px27dr5HJ2IiMgfaeDuUuTmFQ1l0rB2NR8jkcpi2rRptGnThhEjRhQrv/DCC5UsiohIwipTwmhmK83snHgFk8jqVVeTtJTfhg0buPrqqznttNP44YcfGDx4MCtXrvQ7LBERkYiUtUm6Kd5g3VVO0/3UJC3l88orr9CvXz9+//13AGrWrMlDDz1E06ZN/Q1MREQkQuW5h9GF36VyqZ6RykG19vE7DEkyv/76KzfccAOvvvpqsOz0009n7Nixwan+REREkkF5EsaBZtY7wn2dc65bOZ4joaSlmN8hSJL573//S58+fdi0aRMA9erV4/HHH+eKK67ATJ8nERFJLuVJGFsFlkhUitrIVP2BlzLKyMgIJouXXnopo0aNokGDBj5HJSIiUj7lSRi7O+cmxzySBLZvRlIPJykVwDlXrObwvPPO48Ybb6Rbt26ce+65PkYmIiISPQ2rE4FGdTSkjuzd119/zXHHHcfMmTOLlY8aNUrJooiIVApKGCNQU9MCyh7s3r2boUOH0r59e2bOnEmvXr3YuXOn32GJiIjEXFmbpJsBa+MRSCLLTFPCKMXNmzePa6+9loULFwJgZpx++uk4Vylu2xURESmmTAmjc+6HeAWSyDLTVBErnh07djB48GBGjBhBfn4+AFlZWWRnZ9O5c2efoxMREYkPzSUdgcxUJYwCM2bMoFevXixfvhyAtLQ07rnnHu655x4yMzN9jk5ERCR+lDBGQDWMAt6MLYXJYocOHZg4cSJt27b1OSoREZH4UyYUASWMAjB06FBatWrFiBEjmDVrlpJFERGpMpQJRUAJY9Wzfv16brzxRjZu3Bgsq1GjBgsXLuT2228nLU2V8yIiUnXor14ElDBWHc45XnrpJW644QbWrl1LTk4O2dnZwe1KFEVEpCpSJhQBJYxVwy+//MIFF1zApZdeytq13uhRa9euZffu3T5HJiIi4i9lQhFQwli5OefIzs4mKyuL119/HYD69evzn//8hzfeeIP09HSfIxQREfGX2tcikKFhdSqtlStXct111/HBBx8Ey6644goef/xx6tev72NkIiIiiUMJYwRUw1h5XX311XzyyScANGzYkDFjxnDWWWf5HJWIiEhiUSYUASWMldeoUaNIT0+nT58+LFmyRMmiiIjIHqiGMQKZqZpLujLIzc3lxRdfpHv37pgZAO3bt2f58uU0adLE5+hEREQSV8JVnZlZfzNbbWZrzGyImUUco5n9xczmm1mumW0ys4fLcvzeqIYx+c2dO5cOHTpw1VVX8fzzzxfbpmRRRESkdAmVCZnZLcDDwATgGuBy4I4Ijz0CmAKsAa4H/hs4tm+0cSlhTF45OTnccccddO7cmUWLFgGQnZ2Nc87nyERERJJHwjRJm1kGMAgY45wbHCjLAV4zs0ecc3lhTjEQ+BA4x3nZQLaZtQIuBp6MJjYljMlp+vTp9O7dmxUrVgCQnp7OwIEDGTBgQLBJWkRERMJLmIQR6ADUASYVFjjnPgr8Ye8IzApz/CBgmytedbQeOCDawJQwJpfNmzdz1113MXbs2GBZp06dyM7OpnXr1j5GJiIikpwSKWE8OPBzYYny1UALwiSMzrnvQtfNrDpwPDCxtOPMrAGwf4niw0JXlDAmj/z8fDp37sw333wDQLVq1Rg6dCg33XQTqeq8JCIiUi6JlAlVA/Kdc1tLlO/gjwldJG4D9gWeCLNfX2BxieWN0B2UMCaP1NRU+vXrB8BJJ53E4sWLufXWW5UsioiIRCGRahh3AXu6TzEXL5mMmJm1xruncaRz7ocwuz8FvFSi7DBCkkYljInLOce3337L4YcfHizr27cvDRs25LzzztO9iiIiIjGQSJnQGiDTzOqVKN8P2B7pScxsH7z7IL8G7g+3v3NujXNuSegCFGveztTUgAnpp59+4pxzzqF9+/Z8++23wfKUlBTOP/98JYsiIiIxkkiZ0Hy82sRjCwvMrCbQEvilDOd5CmgGXOqc2xWLwDJUw5hQCgoKGDduHK1bt2bKlCns3LmTAQMG+B2WiIhIpZUwmZBzbjPwHtDfzApvOOsHGN5wOWGZ2d3A1cA1zrlvw+weMTVJJ44VK1bQrVs3+vTpw5YtWwDo3r0748aN8zkyERGRyiuR7mEEb2icT4HZZrYKuAgY5ZxbG2iqbgbMd87llzzQzI4DHgBeB340sw6F25xzX0QTlJqk/Zefn89jjz3Gvffey44dOwBo1KgRY8eO5YwzzvA5OhERkcotoRJG59w8MzsGGAIcCtwFjAxsPht4BqgLbNrDgsz/cgAAGJRJREFU4Rfh1ZieH1hCRXUzW2aaetj6aevWrXTr1o25c+cGy/r27cuwYcOoVauWj5GJiIhUDQlXdeacW+ScO88518E598/C2kTn3LPOOXPO7SlZxDl3c2D7H5ZoY1KTtL9q1qzJIYccAkCLFi34+OOPGT16tJJFERGRCpJQNYyJKj1VvW0r2q5du8jMzAyujx49mqysLO655x6qVSvTKEsiIiISJVWdhZGZlqLhWSrQ9u3bue222+jYsSO7dhV1cj/wwAMZMmSIkkUREREfKGEMY98M3b9YUT788EP+9Kc/8eijj7Jo0SIefPBBv0MSERERlDCGVSNTrfbxtmnTJnr37k23bt1YuXIlAF26dOGyyy7zOTIREREBJYxhqYYxvt58801at27NhAkTANh3330ZNWoUn3zyCa1atfI5OhEREQF1eglLCWN8bNiwgb59+/Liiy8Gy04++WTGjRtH06ZN/QtMRERE/kA1jGHUVJN0XKSnpzNr1iwA6tSpwzPPPMO0adOULIqIiCQgJYxh1MhQwhgPNWvWZOzYsZx//vksXbqUq6++Wr3RRUREEpQSxjCqq0k6agUFBYwZM4bbbrutWPlpp53Gq6++ykEHHeRTZCIiIhIJVZ+FoVleorN8+XJ69erFjBkzADjrrLM46aSTfI5KREREykLZUBjpKXqLyiMvL4+HH36YP/3pT8FksXHjxqTo/RQREUk6qmEMQ9MClt2CBQvo2bMn8+bNA8DM6NevHw8++CA1a9b0OToREREpKyWMYaSlKGGM1K5du3jggQcYPnw4eXl5ABx++OFMmDCBP//5zz5HJyIiIuWl9sEw0lTDGLFvvvmGYcOGkZeXR2pqKnfffTdfffWVkkUREZEkpxrGMNJ0z13E2rVrR//+/Xn33XfJzs7myCOP9DskERERiQFlQ2GkamzAvXrvvfd44403ipUNHjyYzz//XMmiiIhIJaKEMYxUvUN/sHHjRq699lpOOeUUevbsydq1a4PbMjMzSU9P9zE6ERERiTWlQ2GoSbq4V199laysLJ555hnA6+gyf/58n6MSERGReFI2FEaqekkD8Ntvv3HRRRdx4YUX8ttvvwFw6qmnsnjxYk455RSfoxMREZF4UsIYRlXPF51zPPfcc2RlZfHKK68AULduXZ577jmmTp1KkyZNfI5QRERE4k29pMOo6jWMr776KldffXVw/aKLLuLJJ5/kgAMO8C8oERERqVCqYQyjqveSPu+88+jSpQsHHnggr7zyCi+99JKSRRERkSpGNYxhVLUaxmXLlpGRkUGzZs0ASE1NZfLkydSuXZu6dev6HJ2IiIj4QTWMYVSVhHH37t0MGzaMdu3a0bNnT5xzwW1NmzZVsigiIlKFKWEMoyo0Sc+fP59OnTpxzz33sGvXLqZPn86sWbP8DktEREQShBLGMCpzDePOnTu555576NixY3AsxVatWjFz5ky6du3qc3QiIiKSKHQPYxiVNV+cOXMmPXv2ZNmyZQCkpaVx99138/e//53MzEyfoxMREZFEooQxjMpYwzhx4kR69eoVvE/x6KOPJjs7m3bt2vkcmYiIiCQiNUmHURnvYTzttNOoVasW++yzDw8//DCzZ89WsigiIiJ7pRrGMCpDDeOGDRuoUaMGGRkZABx88MFMnjyZ5s2b07JlS5+jExERkUSnGsYwkjlfdM7x8ssv06pVK4YPH15s2xlnnKFkUURERCKihDEMS9Im6V9//ZULL7yQiy++mDVr1vDAAw+watUqv8MSERGRJKSEMYxkSxedczzzzDNkZWXx2muvAVCvXj2eeeYZmjZt6m9wIiIikpR0D2MYyVTBuGrVKq677jref//9YNlll13G448/ToMGDXyMTERERJKZEsYwLEnqGJ977jn69u1LTk4O4HVsefrppznnnHN8jkxERESSnZqkw0iWGsb69esHk8XevXuzZMkSJYsiIiISE6phDCMlQTPGgoICUlKK8v0zzzyTu+66i1NOOYWTTjrJx8hERESkslENYxiJmC/OmzePo48+munTpxcrHz58uJJFERERiTkljGEkUr64Y8cOBgwYQKdOnfjqq6/o3bt3sBlaREREJF7UJB1GotQwzpgxg169erF8+XIA0tLSuPLKK0lNTfU5MhEREanslDCG4Xcv6S1btjBgwACefvrpYFnHjh3Jzs6mbdu2PkYmIiIiVYWapMPws4Zx6tSptGnTJpgsVqtWjREjRjBr1iwliyIiIlJhVMMYhp+9pD/88ENWr14NwIknnsj48eNp3ry5b/GIiIhI1aQaxjD8bJAePHgwRx99NOPGjeODDz5QsigiIiK+UMIYRkVVMP7888/06NGDdevWBcuqV6/OnDlz6N27d7ExF0VEREQqkpqkw7A4Z4zOOSZMmED//v3ZsmULBQUF/Pvf/w5uV6IoIiIiflPCGEY808XvvvuO3r1789FHHwXLCgoK2L17N+np6XF8ZhEREZHIqfoqjHhUMObn5zNy5Ejatm0bTBYbNmzIW2+9xaRJk5QsioiISEJRDWMYsc4XFy9eTM+ePZkzZ06wrE+fPjz00EPUrl07xs8mIiIiEj0ljGHEelid66+/PpgsNm/enPHjx3PiiSfG9DlEREREYklN0mHEukn66aefplq1atxxxx0sWLBAyaKIiIgkPNUwhpGRWv6cOicnh3/961/06dMn2Nu6TZs2rFq1igMOOCBWIYqIiIjElRLGMDLTypcwTp8+nV69evHdd9+RkZHBtddeG9ymZFFERESSiZqkw0gr4ziImzdvpk+fPvzlL3/hu+++A+Cll17COReP8ERERETiTgljGClluIdxypQptG7dmnHjxgHeTC2PPvooU6ZMifsA4CIiIiLxoibpMCLpJb127Vpuvvlm/vOf/wTLunXrxrhx4zj00EPjGZ6IiIhI3ClhDCNci3ReXh5dunQJNj/Xrl2bRx55hGuvvVa1iiIiIlIpqEk6jHA1jGlpadx5550AnHvuuSxdupSePXsqWRQREZFKQzWMYZRMGAsKCliwYAFHHnlksKxXr140a9aMv/71r0oURUREpNJJuBpGM+tvZqvNbI2ZDTGziGM0swPN7BUz22xmi8zshGjjCe30smLFCrp160bnzp1ZunRp0T4pKZx88slKFkVERKRSSqiE0cxuAR4GJgDXAJcDd0R4bCrwNtARuBp4BphiZs2iiSnFjLy8PEaMGEHbtm2ZPn06ubm53HfffdGcVkRERCRpJEyTtJllAIOAMc65wYGyHOA1M3vEOZcX5hQXAkcBnZxzcwLHtwP6A/3KG9fyr5fwf3fcyNy5c4Nlffv2ZdiwYeU9pYiIiEhSSZiEEegA1AEmFRY45z4KNPN2BGaFOf6vwIrCZDHgdWB4uSP66m3O/+st5OV5uWqLFi3Izs7muOOOK/cpRURERJJNIjVJHxz4ubBE+WqgRYTHlzz2R+DQQHN12S2cSl5eHqmpqQwYMIAFCxYoWRQREZEqJ5FqGKsB+c65rSXKdwD7R3j8b3s4Ng2v5nL9ng4yswZ7OP8RhQ8Obd6CYUMfoHXr1qxcuTKCMEREREQqzooVKwofZsTrORIpYdwF7Ok+xVy8ZDCS4/P3cCxhju+Ld+/kHq1csZxLL700gqcXERER8VUbYH48TpxICeMaINPM6jnnQmsD9wO2R3j8wSXK9gv8LO34p4CXSpS1Bf4DXAR8E8FzS2I5DHgDOBf4zudYpOx0/ZKXrl1y0/VLXkcALwPfxusJEilhnI9XI3gs8CaAmdUEWgK/RHD8bOAfZpYW0qP6aLxm6U17O8g5twYv2QwKGU/xG+fckjK8BkkAIdfvO12/5KPrl7x07ZKbrl/yCrl22+L1HAnT6cU5txl4D+gf0kmlH2DAhxGc4lWgHnAtBIfp+RvwgXPOxT5iERERkaohkWoYwbuX8FNgtpmtwmsSHuWcW2tm9YBmwHznXMl7FXHOrTGzR4AnzawbcDiQBfSsuPBFREREKp+EqWEEcM7NA44BfgYOBe4Cbg9sPhuYC9Qs5fg78TqxNAHWAic4576IZ8wiIiIilV2i1TDinFsEnLeH8meBZyM4fgLe1ILRWAsMDvyU5KPrl9x0/ZKXrl1y0/VLXnG/dqbb+0RERESkNAnVJC0iIiIiiUcJo4iIiIiUSgmjiIiIiJRKCaOIiIiIlKpKJoxm1t/MVpvZGjMbYmYRvw9mdqCZvWJmm81skZmdEM9Ypbgor91fzGy+meWa2SYze7gsx0v0orl+Jc7TwczyzKxpbCOUvYnFtTOzNma228wuikeMsndR/u48yczmmVmOmX1vZn+3kKlFpGKY2clmVuYpG82spZm9Z2ZbzWy2mbUpz/NXuT+WZnYL8DDe0DvXAJcDd0R4bCrwNtARuBp4BphiZs3iEqwUE+W1OwKYgjcN5PXAfwPH9o1LsPIH0Vy/EufJACYCqeH2ldiIxbULJBhjgRnOuZdjHqTsVZS/Ow8EXsGbY/pUYBTeJBv94hKs7FHgb9hkyvh7z8xqAe8DDYBLgenAe2ZWp8xBOOeqzAJkABuBp0LK/oI313RaBMdfAjjgmJCy54DRfr+2yr7E4No9D7xFYCipQNknwMd+v7aqsER7/Uqc6x/A5sB3sanfr62yL7G6dsB1wG6gtd+vqSotMfjd2RNYUqLseeB9v19bVVnwJjRZD8wBvi/jsXcCuUCjkLKPgTvKGkdVq2HsANQBJhUWOOc+CjzsGMHxfwVWOOfmhJS9HiiX+Ir22g0CernAtyVgPd4vU4m/aK8fAGbWjuIzQEn8RX3tzKwBMBx40jm3JOYRSmmivX71+WNrZAawMybRSSSOx/ud91Q5jv0rXsXITyFl5cpbqlrCeHDg58IS5auBFhEeX/LYH4FDA83VEj9RXTvn3HfOud8L182sOt6XcGbMIpTSRPvdw8zS8G4DmYjXxCIVI+prB/wTqAvUM7Pnzexvun+4wkR7/T4AWppZXzOrYWanA+fg1TJKxRjpvNnuymNveUuk392gqvaFrQbkO+e2lijfAewf4fEb93BsGt5/cBI/0V67km4D9gWeiDYwiUgsrt9deN+zMt/3KFGJ6tqZWVvg/4B8oClwBPA08Ko6TlSIqK6fc+4LvBaa0cBW4B28muIXYh2o7JlzriCKw/eWt5T572ZVSxh3AXl7KM/Fe1MjOT5/D8cS4fFSftFeuyAzaw0MxPuv7YcYxCbhRXX9zCwL75pd45zbFuPYpHTRfveuAwy4yDl3vHOuA3ATcC5wcsyilL2J9rvXEe+ftUeBi4FHgJvNbEAsg5S42VveUuacpaoljGuATDOrV6J8P2B7hMcfXKJsv8DPSI6X8ov22gFgZvvg3cvzNXB/zKKTcMp9/QK3e0wExjvnPo5TfLJ30X73WgLLnXOvh5RNAAqAI2MTopQi2ut3LzDROXebc+5l51x/4AHgfjOrGeNYJfb2lreUOWepagnjfLzM+tjCgsAHviXwSwTHzwY6Be6lKnQ0XvXuphjGKX8U7bUr9BTQDLjUObcrphFKaaK5focAnYAbzcyZmQNWBbatMrPpsQ9XQkT73dsOrCxRlouXMOo7GH/RXr+WQMmx/xYCmUCjGMUo8TObkGsfcDRl+7sJVLGE0Tm3GXgP6B/SSaUfXnPJhxGc4lWgHnAtBMeD+xvwQYnetxJjMbh2mNndeONnXuOc+zYeccqeRXn9fsGriQpdzgxsOxPoFfOAJSgG3725QJaZpYeUHYt37/fnsYxV/igG128dXk/rUGfhJfy/xSpOiZuXgSPN7BQAM6sN9KA8HQf9Hl/Ih/GMCmsE5+IN3lwAPBbYVg/vi5FayvEP4/239iLwFd64Yh38fl1VYYnm2gHH4d3H8Wpgv+Di9+uqKku0370S52qKxmFMimuHd3P9OrzBn7sBVwI/4A3g7ftrqwpLlNfvjsD+LwHDgKmB795zfr+uqrbgVXh8X6LsYKB9mOP+C2wD/g0sB7YAjcv6/FWqhhHAOTcPbxDMn4FDKT6m29l4X6i93pfhnLsTb3aQJsBa4ATn9SKTOIvy2l2EV6N+fmC/0EUqQLTfPfFPNNfOObcW+DPe9++/eL1t5+J9J6UCRPndewS4GWgV+HkM3oQVN8YxZIncdXizt5TmCmAI3jVcCnR2zv1Y1ieyQPYpIiIiIrJHVa6GUURERETKRgmjiIiIiJRKCaOIiIiIlEoJo4iIiIiUSgmjiIiIiJRKCaOIiIiIlEoJo4iIiIiUSgmjiIiIiJRKCaNIFWVmTc3MhVn2ieL8zsyejWHIUdnL691qZu+ZWecKjuH+RDxfLJjZiSXe43wz+8nM3jCzrn7HJyLlk+Z3ACLiu0+AcXvZlluRgVSQecBjeL//mgG9gI/N7Djn3Jw4P/da4P+AhaXtFEgAv3LOvR6L8/lkCvAisA9wONAbONPMznTOTSvPCcvwvohIjClhFJGVzrnn/Q6iAv0U+nrN7HlgCfAP4LR4PrFzbjsQyXs9CG++3lITozKczw9LSrzPLwGfA7cC5UoYifB9EZHYU5O0iFRpzrnlwAqgi9+xVGaB2tv1QFOfQxGRclDCKCIRMbMrzWyemW0zs9/M7BUzOzQG59tqZuvMbIqZtdnDfmeb2UwzyzGzX83sRTNrEt2r+YMcILPE815gZl+Y2c7A6x1tZnXL+zoC++71nkMze7bwvr9AUY+Q+wCnl+N8xwe29SxRnmFmG8zs5RLlcX2fzawmUAf4rUR5qZ+rsr4vFfR5EalylDCKSKaZ1S+xVAvdwcx64DV9bsBrUnwEOBaYYmZlvrXFzM4POd/twBCgOfCemVUP2e864E28e/VuBkYBxwNzzGz/sr/UPcZSA2gBfBNSdivwCpAP3AG8gHcP3qdmVrusryNCY/HuR/y/wPonIetDy/zCvOO/By4vUX4qUBf4d2FBnN7naoHP0v5m1gF4CUgt8byRfK4ifl8q4vMiUmU557Ro0VIFF7ymQbeXZUCJfYfgdWDIDCnrG9g3ay/nd8Cze9n2RGB77ZCyDsAYoHFgvQawGa/zRP2Q5ZLAsbeX8/W+EzjPgUBn4O1Aec/Afg2BXXj326WFHH95YL+Hy/I69hLD/WFi3et7V5bzBa5bPnBgSNlkYB2QHqf3+cS9fKY2Rfu5CvOZiunr0KJFS/FFnV5E5F3gnyXKloeuOOfuBTCzhmZ2DHAMRTU+DYClZXzOT4EbgCfM7N/AfOfcF8AXIft0BWoBZ+LVGJXUrozPWej0EufbAdzrnMsOrJ8GZABPOufyCndyzv3HzB4BzgPuLMPr8NO/gYF4SdOoQK3nOXhJ1+7APvF6n18AsoEs4HEg2zk3PHSHGH+u4vU6RAT1khYR+NU5935pO5hZF+ApoD2wBfgKmAOcX54ndM69aGYH4A1pcyWQYmZLgIecc4VNloVNiMOBD/ZwmjXleW7gM7zetg7YDixyXm/jQgcGfq7ew7GrgVZlfB2+cc59a2af49WOjsJLFvclpFmY+L3PPwQ+V++bWXfgb2Y23DkXTOZi/LmK1+sQEXQPo4iEEbjH7y28Wrf2QB3n3Al4TZvl5pwb5Zz7E15TYle8++3+ZWbHBXZZF/i50Tn3fugCbAV2/+GkkVkbOM8HzrnZJZJFgN8DPxvt4dhGIdsjfR1++zfQ2cya4SWO3zrnPg/ZHq/3OdQ/gOpA/8KCOHyuKuJ1iFRZShhFJJzDgXrAa865Bc45Z2bpwDXlPaGZvW9mMwCcczucc7Pwmk4BOgZ+fob3h/7ywPMVHnsMMBuvVi8epuElF33NLDXkeS8BDsbrVFGW11Ee64HaYfeKzAsEXg9ec3vJms+4v8/OuSl4A6b3NbN6geLyfK5Ke1/8+ryIVAlqkhaRcL7Da7rtaWa/483c0QNoGdhebW8HlmImcJ+ZvYJ3D2Uq0BOvg8YnAM65rWbWH6+X7Odm9hyQjtf7dQ1eE2vMOedWm9nfgYfxekVPAg7Fu1dxGfBAWV5HOb0FXGFm9wI/4d0H+JVzblI5Xs96M3sHr3bPUWKg7wp8n/8BvIHXm/weyve52uv74tfnRaTK8LvXjRYtWvxZKOph+2wE+/4Fr5YmB69JdgJwVuD40Xs5prQerSlAP+BLvJ6tW/CSrzP2sO85eLVHOXh/+P8LNI/i9b4e4f4X49WK7Qq85qeB/cr7OkrEcH+Y566F19P6VyAPr5n77CjOd2Fgvxml7BOr9/nEwHMN38O2LwPv0X7l+VxF8r7E6nVo0aKl+GLOFY6FKiIiIiLyR7qHUURERERKpYRRREREREqlhFFERERESqWEUURERERKpYRRREREREqlhFFERERESqWEUURERERKpYRRREREREqlhFFERERESqWEUURERERKpYRRREREREqlhFFERERESvX/F8mwFnQru0wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15dbf200160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15dccd7c7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluation.plot_all()"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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   "number_sections": true,
   "sideBar": true,
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